—
—2025—————————
—
[324] Jimenez-Varon, C. F., Lee, H., Genton, M. G., and Sun, Y. (2025), “Visualization and assessment of copula symmetry,” Journal of Computational and Graphical Statistics, to appear.
[323] Martinez-Hernandez, I., and Genton, M. G. (2025), “Functional time series analysis and visualization based on records,” Journal of Computational and Graphical Statistics, to appear.
[322] Nag, P., Hong, Y., Abdulah, S., Qadir, G. A., Genton, M. G., and Sun, Y. (2025), “Efficient large-scale nonstationary spatial covariance function estimation using convolutional neural networks,” Journal of Computational and Graphical Statistics, to appear.
[321] Ojo, O. T., and Genton, M. G. (2025), “Functional multiple-point simulations,” Computers and Geosciences, to appear.
[320] Qu, Z., Dai, W., Euan, C., Sun, Y., and Genton, M. G. (2025), “Exploratory functional data analysis (with discussion),” TEST, to appear.
[319] Qu, Z., Dai, W., and Genton, M. G. (2025), “Robust two-layer partition clustering of sparse multivariate functional data,” Econometrics and Statistics, to appear.
[318] Song, Y., Dai, W., and Genton, M. G. (2025), “Large-scale low-rank Gaussian process prediction with support points,” Journal of the American Statistical Association, to appear.
[317] Song, Y., Khalid, Z., and Genton, M. G. (2025), “Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations from CESM2-LENS,” Journal of the American Statistical Association, to appear.
—
—2024—————————
—
[316] Abdulah, S., Baker, A. H., Bosilca, G., Cao, Q., Castruccio, S., Genton, M. G., Keyes, D. E., Khalid, Z., Ltaief, H., Song, Y., Stenchikov, G. L., and Sun, Y. (2024), “Boosting earth system model outputs and saving petabytes in their storage using exascale climate emulators,” SC24 (GB Climate Modelling finalist), to appear.
[315] Abdulah, S., Ejarque, J., Marzouk, O., Ltaief, H., Sun, Y., Genton, M. G., Badia, R. M., and Keyes, D. E. (2024), “Portability and scalability evaluation of large-scale statistical modeling and prediction software through HPC-ready containers,” Future Generation Computer Systems, 161, 248-258.
[314] Chen, S., Abdulah, S., Sun, Y., and Genton, M. G. (2024), “On the impact of spatial covariance matrix ordering on tile low-rank estimation of Matern parameters,” Environmetrics, 35:e2868.
[313] Chowdhury, J., Khalid, Z., and Genton, M. G. (2024), “Fast and accurate spherical harmonic transform for spatio-temporal regular grid data,” IEEE Signal Processing Letters, 31, 1825-1829.
[312] Hu, Z., Tong, T., and Genton, M. G. (2024), “A pairwise Hotelling method for testing high-dimensional mean vectors,” Statistica Sinica, 34, 229-256.
[311] Karling, M., Durante, D., and Genton, M. G. (2024), “Conjugacy properties of multivariate unified skew-elliptical distributions,” Journal of Multivariate Analysis, 204:105357.
[310] Mondal, S., Arellano-Valle, R. B., and Genton, M. G. (2024), “A multivariate modified skew- normal distribution,” Statistical Papers, 65, 511-555.
[309] Mondal, S., and Genton, M. G. (2024), “A multivariate skew-normal-Tukey-h distribution,” Journal of Multivariate Analysis, 200:105260.
[308] Mondal, S., Krupskii, P., and Genton, M. G. (2024), “A non-stationary factor copula model for non-Gaussian spatial data,” Stat, 13:e715.
[307] Pan, Q., Abdulah, S., Genton, M. G., Keyes, D. E., Ltaief, H., and Sun, Y. (2024), “GPU- accelerated Vecchia approximations of Gaussian processes for geospatial data using batched matrix computations,” ISC High Performance Research Paper Proceedings, 39th International Conference, Hamburg, Germany, pp. 1-12.
[306] Wang, K., Karling, M., Arellano-Valle, R. B., and Genton, M. G. (2024), “Multivariate unified skew-t distributions and their properties,” Journal of Multivariate Analysis, 203:105322.
[305] Zhang, J., Crippa, P., Genton, M. G., and Castruccio, S. (2024), “Sensitivity analysis of wind energy resources with Bayesian non-Gaussian and non-stationary functional ANOVA,” Annals of Applied Statistics, 18, 23-41.
[304] Zhang, X., Abdulah, S., Ltaief, H., Sun, Y., Genton, M. G., and Keyes, D. E. (2024), “Parallel approximations for high-dimensional multivariate normal probability computation in confidence region detection applications,” IEEE International Parallel and Distributed Processing Symposium (IPDPS), San Francisco, CA, USA, pp. 265-276.
—
—2023—————————
—
[303] Abdulah, S., Li, Y., Cao, J., Ltaief, H., Keyes, D. E., Genton, M. G., and Sun, Y. (2023), “Large-scale environmental data science with ExaGeoStatR,” Environmetrics, 34:e2770. (cover)
[302] Cao, Q., Abdulah, S., Ltaief, H., Genton, M. G., Keyes, D. E., and Bosilca, G. (2023), “Reducing data motion and energy consumption of geospatial modeling applications using automated precision conversion,” IEEE International Conference on Cluster Computing, 330-342.
[301] Chen, W., and Genton, M. G. (2023), “Are you all normal? It depends!,” International Statistical Review, 91, 114-139.
[300] Das, S., Alshehri, Y. M., Stenchikov, G. L., and Genton, M. G. (2022), “A space-time model with temporal cyclostationarity for probabilistic forecasting and simulation of solar irradiance data,” Stat, 12:e583.
[299] Hong, Y., Song, Y., Abdulah, S., Sun, Y., Ltaief, H., Keyes, D. E., and Genton, M. G. (2023), “The third competition on spatial statistics for large datasets,” Journal of Agricultural, Biological, and Environmental Statistics, 28, 618-635.
[298] Huang, H., Castruccio, S., Baker, A., and Genton, M. G. (2023), “Saving storage in climate ensembles: A model-based stochastic approach (with discussion),” Journal of Agricultural, Biological, and Environmental Statistics, 28, 324-344. (Discussion 1,2,3,4,5,rejoinder)
[297] Huang, H., Sun, Y., and Genton, M. G. (2023), “Test and visualization of covariance properties for multivariate spatio-temporal random fields,” Journal of Computational and Graphical Statistics, 32, 1545-1555.
[296] Karling, M., Genton, M. G., and Meintanis, S. G. (2023), “Goodness-of-fit tests for multivariate skewed distributions based on the characteristic function,” Statistics and Computing, 33:99.
[295] Martinez-Hernandez, I., and Genton, M. G. (2023), “Surface time series models for large nonstationary spatio-temporal datasets,” Spatial Statistics, 53:100718.
[294] Mondal, S., Abdulah, S., Ltaief, H., Sun, Y., Genton, M. G., and Keyes, D. E. (2023), “Tile low-rank approximations of non-Gaussian spatial and space-time Tukey g-and-h random field likelihoods and predictions on large-scale systems,” Journal of Parallel and Distributed Computing, 180, 104715.
[293] Ojo, O. T., Fernandez Anta, A., Genton, M. G., and Lillo, R. E. (2023), “Multivariate functional outlier detection using the fast massive unsupervised outlier detection indices,” Stat, 12:e567.
[292] Porcu, E., White, P., and Genton, M. G. (2023), “Stationary non-separable space-time covariance functions on networks,” Journal of the Royal Statistical Society - Series B, 85, 1417-1440.
[291] Salvana, M. L., Lenzi, A., and Genton, M. G. (2023), “Spatio-temporal cross-covariance functions under the Lagrangian framework with multiple advections,” Journal of the American Statistical Association, 118, 2746-2761.
[290] Wang, K., Abdulah, S., Sun, Y., and Genton, M. G. (2023), “Which parametrization of the Matern covariance function?,” Spatial Statistics, 58:100787.
[289] Wang, K., Arellano-Valle, R. B., Azzalini, A., and Genton, M. G. (2023), “On the nonidentiability of unified skew-normal distributions,” Stat, 12:e597.
[288] Zhang, Z., Arellano-Valle, R. B., Genton, M. G., and Huser, R. (2023), “Tractable Bayes of skew-elliptical link models for correlated binary data,” Biometrics, 79, 1788-1800.
—
—2022—————————
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[287] Abdulah, S., Castruccio, S., Genton, M. G., and Sun, Y. (2022), “Editorial: Large-scale spatial data science,” Journal of Data Science, 20, 437-438.
[286] Abdulah, S., Alamri, F., Nag, P., Sun, Y., Ltaief, H., Keyes, D. E., and Genton, M. G. (2022), “The second competition on spatial statistics for large datasets,” Journal of Data Science, 20, 439-460.
[285] Abdulah, S., Cao, Q., Pei, Y., Bosilca, G., Dongarra, J., Genton, M. G., Keyes, D. E., Ltaief, H., and Sun, Y., (2022), “Accelerating geostatistical modeling and prediction with mixed-precision computations: A high-productivity approach with PaRSEC,” IEEE Transactions on Parallel and Distributed Systems, 33, 964-976.
[284] Bastos, F., Barreto-Souza, W., and Genton, M. G. (2022), “A generalized Heckman model with varying sample selection bias and dispersion parameters,” Statistica Sinica, 32, 1911-1938.
[283] Cao, J., Durante, D., and Genton, M. G. (2022), “Scalable computation of predictive probabilities in probit models with Gaussian process priors,” Journal of Computational and Graphical Statistics, 31, 709-720.
[282] Cao, J., Genton, M. G., Keyes, D., and Turkiyyah, G. (2022), “tlrmvnmvt: Computing highdimensional multivariate normal and Student-t probabilities with low-rank methods in R,” Journal of Statistical Software, 101:4.
[281] Cao, J., Guinness, J., Genton, M. G., and Katzfuss, M. (2022), “Scalable Gaussian-process regression and variable selection using Vecchia approximations,” Journal of Machine Learning Research, 23 (348), 1-30.
[280] Cao, Q., Abdulah, S., Alomairy, R., Pei, Y., Nag, P., Bosilca, G., Dongarra, J., Genton, M. G., Keyes, D. E., Ltaief, H., and Sun, Y. (2022), “Reshaping geostatistical modeling and prediction for extreme-scale environmental applications,” in International Conference for High Performance Computing, Networking, Storage and Analysis (SC22), Dallas, TX, US, 13-24.
[279] Chowdhury, J., Dutta, S., Arellano-Valle, R. B., and Genton, M. G. (2022), “Sub-dimensional Mardia measures of multivariate skewness and kurtosis,” Journal of Multivariate Analysis, 192:105089.
[278] Genton, M. G., and Sun, Y. (2022), “Functional data visualization,” in Piegorsch, W. W., Levine, R. A., Zhang, H. H., and Lee, T. C. M. (eds), Computational Statistics in Data Science, pp. 457-467, Chichester: John Wiley & Sons, ISBN: 978-1-119-56107-1.
[277] Giani P., Genton, M. G., and Crippa, P. (2022), “Modeling the convective boundary layer in the Terra Incognita: Evaluation of different strategies with real-case simulations,” Monthly Weather Review, 150, 981-1001.
[276] Huang, H., Castruccio, S., and Genton, M. G. (2022), “Forecasting high-frequency spatiotemporal wind power with dimensionally reduced echo state networks,” Journal of the Royal Statistical Society - Series C, 71, 449-466.
[275] Ltaief, H., Genton, M. G., Gratadour, D., Keyes, D. E., and Ravasi, M. (2022), “Responsibly reckless matrix algorithms for HPC scientific applications,” Computing in Science & Engineering, 24, 12-22.
[274] Mondal, S., Abdulah, S., Ltaief, H., Sun, Y., Genton, M. G., and Keyes, D. E. (2022), “Parallel approximations of the Tukey g-and-h likelihoods and predictions for non-Gaussian geostatistics,” International Parallel and Distributed Processing Symposium, 379-389.
[273] Qu, Z., and Genton, M. G. (2022), “Sparse functional boxplots for multivariate curves,” Journal of Computational and Graphical Statistics, 31, 976-989.
[272] Salvana, M. L., Abdulah, S., Ltaief, H., Sun, Y., Genton, M. G., and Keyes, D. E. (2022), “Parallel space-time likelihood optimization for air pollution prediction on large-scale systems,” in Platform for Advanced Scientific Computing Conference (PASC ‘22), Basel, Switzerland, Article No. 17, 1-11.
—
—2021—————————
—
[271] Arellano-Valle, R. B., Harnik, S. B., and Genton, M. G. (2021), “On the asymptotic joint distribution of multivariate sample moments,” in Advances in Statistics - Theory and Applications: Honoring the Contributions of Barry C. Arnold in Statistical Science, I. Ghosh, N. Balakrishnan, H. K. T. Ng (eds), 181-206.
[270] Cao, J., Genton, M. G., Keyes, D., and Turkiyyah, G. (2021), “Sum of Kronecker products representation and its Cholesky factorization for spatial covariance matrices from large grids,” Computational Statistics and Data Analysis, Annals of Statistical Data Science, 157:107165.
[269] Cao, J., Genton, M. G., Keyes, D., and Turkiyyah, G. (2021), “Exploiting low-rank covariance structures for computing high-dimensional normal and Student-t probabilities,” Statistics and Computing, 31:2, 1-16.
[268] Chen, W., Castruccio, S., and Genton, M. G. (2021), “Assessing the risk of disruption of wind turbine operations in Saudi Arabia using Bayesian spatial extremes,” Extremes, 24, 267-292.
[267] Chen, W., Genton, M. G., and Sun, Y. (2021), “Space-time covariance structures and models,” Annual Review of Statistics and Its Application, 8, 191-215.
[266] Crippa, P., Alifa, M., Bolster, D., Genton, M. G., and Castruccio, S. (2021), “A temporal model for vertical extrapolation of wind speed and wind energy assessment,” Applied Energy, 301:117378.
[265] Dao, A., and Genton, M. G. (2021), “Skew-elliptical cluster processes,” in Advances in Statistics - Theory and Applications: Honoring the Contributions of Barry C. Arnold in Statistical Science, I. Ghosh, N. Balakrishnan, H. K. T. Ng (eds), 365-393.
[264] Das, S., Genton, M. G., Alshehri, Y. M., and Stenchikov, G. L. (2021), “A cyclostationary model for temporal forecasting and simulation of solar global horizontal irradiance,” Environmetrics, 32:e2700.
[263] Das, S., and Genton, M. G. (2021), “Cyclostationary processes with evolving periods and amplitudes,” IEEE Transactions on Signal Processing, 69, 1579-1590.
[262] Hong, Y., Abdulah, S., Genton, M. G., and Sun, Y. (2021), “Efficiency assessment of approximated spatial predictions for large datasets,” Spatial Statistics, 43:100517.
[261] Huang, H., Abdulah, S., Sun, Y., Ltaief, H., Keyes, D. E., and Genton, M. G. (2021), “Competition on spatial statistics for large datasets (with discussion),” Journal of Agricultural, Biological, and Environmental Statistics, 26, 580-595. (Discussion 1,2,3,4,5,6,rejoinder)
[260] Huang, J., Cao, J., Fang, F., Genton, M. G., Keyes, D. E., and Turkiyyah, G. (2021), “An O(N) algorithm for computing expectation of N-dimensional truncated multi-variate normal distribution I: Fundamentals,” Advances in Computational Mathematics, 47:65.
[259] Krupskii, P., and Genton, M. G. (2021), “Conditional normal extreme-value copulas,” Extremes, 24, 403-431.
[258] Lenzi, A., Castruccio, S., Rue, H., and Genton, M. G. (2021), “Improving Bayesian local spatial models in large data sets,” Journal of Computational and Graphical Statistics, 30, 349-359.
[257] Martinez-Hernandez, I., and Genton, M. G. (2021), “Nonparametric trend estimation in functional time series with application to annual mortality rates,” Biometrics, 77, 866-878.
[256] Qu, Z., Dai, W., and Genton, M. G. (2021), “Robust functional multivariate analysis of variance with environmental applications,” Environmetrics, 32:e2641.
[255] Salvana, M. L., Abdulah, S., Huang, H., Ltaief, H., Sun, Y., Genton, M. G., and Keyes, D. E. (2021), “High performance multivariate geospatial statistics on manycore systems,” IEEE Transactions on Parallel and Distributed Systems, 32, 2719-2733.
[254] Salvana, M., and Genton, M. G. (2021), “Lagrangian spatio-temporal nonstationary covariance functions,” in Advances in Contemporary Statistics and Econometrics - Festschrift in Honor of Christine Thomas-Agnan, A. Daouia, A. Ruiz-Gazen (eds), 427-447.
[253] Yan, Y., Huang, H.-C., and Genton, M. G. (2021), “Vector autoregressive models with spatially structured coefficients for time series on a spatial grid,” Journal of Agricultural, Biological, and Environmental Statistics, 26, 387-408.
[252] Zhang, J., Crippa, P., Genton, M. G., and Castruccio, S. (2021), “Assessing the reliability of wind power operations under a changing climate with a non-Gaussian bias correction,” Annals of Applied Statistics, 15, 1831-1849.
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—2020—————————
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[251] Bachoc, F., Genton, M. G., Nordhausen, K., Ruiz-Gazen, A., and Virta, J. (2020), “Spatial blind source separation,” Biometrika, 107, 627-646.
[250] Dai, W., Mrkvicka, T., Sun, Y., and Genton, M. G. (2020), “Functional outlier detection and taxonomy by sequential transformations,” Computational Statistics and Data Analysis, 149:106960.
[249] Das, S., and Genton, M. G. (2020), “On the stationary marginal distributions of subclasses of multivariate SETAR processes of order one,” Journal of Time Series Analysis, 41, 406-420.
[248] Genton, M. G., and Sun, Y. (2020), “Functional data visualization,” in Wiley StatsRef: Statistics Reference Online, Davidian, M., Kenett, R. S., Longford, N. T., Molenberghs, G., Piegorsch, W. W., and Ruggeri, F. (eds), Chichester: John Wiley & Sons, Article No. stat08290, DOI:10.1002/9781118445112.stat08290.
[247] Giani, P., Tagle, F., Genton, M. G., Castruccio, S., and Crippa, P. (2020), “Closing the gap between wind energy targets and implementation for emerging countries,” Applied Energy, 269:115085.
[246] Lenzi, A., and Genton, M. G. (2020), “Spatio-temporal probabilistic wind vector forecasting over Saudi Arabia,” Annals of Applied Statistics, 14, 1359-1378.
[245] Litvinenko, A., Kriemann, R., Genton, M. G., Sun, Y., and Keyes, D. E. (2020), “HLIBCov: Parallel hierarchical matrix approximation of large covariance matrices and likelihoods with applications in parameter identification,” MethodsX, 7:100600.
[244] Martinez-Hernandez, I., and Genton, M. G. (2020), “Recent developments in complex and spatially correlated functional data,” Brazilian Journal of Probability and Statistics, 34, 204-229.
[243] Porcu, E., Bevilacqua, M., and Genton, M. G. (2020), “Nonseparable, dynamically and compactly supported space-time covariance functions,” Statistica Sinica, 30, 719-739.
[242] Salvana, M. L., and Genton, M. G. (2020), “Nonstationary cross-covariance functions for multivariate spatio-temporal random fields,” Spatial Statistics, 37:100411.
[241] Shi, J., Tong, T., Wang, Y., and Genton, M. G. (2020), “Estimating the mean and variance from the five-number summary of a log-normal distribution,” Statistics and Its Interface, 13, 519-531.
[240] Tagle, F., Castruccio, S., and Genton, M. G. (2020), “A hierarchical bi-resolution spatial skew-t model,” Spatial Statistics, 35:100398.
[239] Tagle, F., Genton, M. G., Yip, A., Mostamandi, S., Stenchikov, G., and Castruccio, S. (2020), “A high-resolution bi-level skew-t stochastic generator for assessing Saudi Arabia’s wind energy resources (with discussion),” Environmetrics, 31:e2628. (discussion 1,2,3,4,rejoinder)
[238] Vettori, S., Huser, R., Segers, J., and Genton, M. G. (2020), “Bayesian model averaging over tree-based dependence structures for multivariate extremes,” Journal of Computational and Graphical Statistics, 29, 174-190.
[237] Yan, Y., Jeong, J., and Genton, M. G. (2020), “Multivariate transformed Gaussian processes,” Japanese Journal of Statistics and Data Science, 3, 129-152.
[236] Yao, Z., Dai, W., and Genton, M. G. (2020), “Trajectory functional boxplots,” Stat, 9:e289.
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—2019—————————
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[235] Abdulah, S., Ltaief, H., Sun, Y., Genton, M. G., and Keyes, D. E. (2019), “Geostatistical modeling and prediction using mixed-precision tile Cholesky factorization,” IEEE 26th International Conference on High-Performance Computing, Data, Analytics, and Data Science, 152-162.
[234] Cao, J., Genton, M. G., Keyes, D., and Turkiyyah, G. (2019), “Hierarchical-block conditioning approximations for high-dimensional multivariate normal probabilities,” Statistics and Computing, 29, 585-598.
[233] Castruccio, S., Genton, M. G., and Sun, Y. (2019), “Visualising spatio-temporal models with virtual reality: From fully immersive environments to apps in stereoscopic view,” Journal of the Royal Statistical Society - Series A, 182, 379-387. (read before the Royal Statistical Society, Discussion and Rejoinder)
[232] Chen, W., and Genton, M. G. (2019), “Parametric variogram matrices incorporating both bounded and unbounded functions,” Stochastic Environmental Research and Risk Assessment, 33, 1669-1679.
[231] Dai, W., and Genton, M. G. (2019), “Directional outlyingness for multivariate functional data,” Computational Statistics and Data Analysis, 131, 50-65.
[230] Genton, M. G., and Sun, Y. (2019), “discussion of Data science, big data, and statistics,” by P. Galeano and D. Pena, TEST, 28, 338-341.
[229] Hernandez-Magallanes, I., and Genton, M. G. (2019), “A point process analysis of cloud-to-ground lightning strikes in urban and rural Oklahoma areas,” Environmetrics, 30:e2535.
[228] Hu, Z., Tong, T., and Genton, M. G. (2019), “Diagonal likelihood ratio test for the equality of mean vectors in high-dimensional data,” Biometrics, 75, 256-267.
[227] Huser, R., Dombry, C., Ribatet, M., and Genton, M. G. (2019), “Full likelihood inference for max-stable data,” Stat, 8:e218.
[226] Jeong, J., Yan, Y., Castruccio, S., and Genton, M. G. (2019), “A stochastic generator of global monthly wind energy with Tukey g-and-h autoregressive processes,” Statistica Sinica, 29, 1105- 1126.
[225] Krupskii, P., and Genton, M. G. (2019), “A copula model for non-Gaussian multivariate spatial data,” Journal of Multivariate Analysis, 169, 264-277.
[224] Litvinenko, A., Sun, Y., Genton, M. G., and Keyes, D. E. (2019), “Likelihood approximation with hierarchical matrices for large spatial datasets,” Computational Statistics and Data Analysis, 137, 115-132.
[223] Martinez-Hernandez, I., Genton, M. G., and Gonzalez-Farias, G. (2019), “Robust depth-based estimation of the functional autoregressive model,” Computational Statistics and Data Analysis, 131, 66-79.
[222] Militino, A. F., Ugarte, M. D., P erez-Goya, U., and Genton, M. G. (2019), “Interpolation of the mean anomalies for cloud-filling in land surface temperature and normalized difference vegetation index,” IEEE Transactions on Geoscience and Remote Sensing, 57, 6068-6078.
[221] Tagle, F., Castruccio, S., Crippa, P., and Genton, M. G. (2019), “A non-Gaussian spatiotemporal model for daily wind speeds based on a multivariate skew-t distribution,” Journal of Time Series Analysis, 40, 312-326.
[220] Vettori, S., Huser, R., and Genton, M. G. (2019), “Bayesian modeling of air pollution extremes using nested multivariate max-stable processes,” Biometrics, 75, 831-841.
[219] Yan, Y., and Genton, M. G. (2019), “The Tukey g-and-h distribution,” Significance, 16(3), 10-11.
[218] Yan, Y., and Genton, M. G. (2019), “Non-Gaussian autoregressive processes with Tukey g-and-h transformations,” Environmetrics, 30, e2503.
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—2018—————————
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[217] Abdulah, S., Ltaief, H., Sun, Y., Genton, M. G., and Keyes., D. E. (2018), “ExaGeoStat: A high performance unified software for geostatistics on manycore systems,” IEEE Transactions on Parallel and Distributed Systems, 29, 2771-2784.
[216] Abdulah, S., Ltaief, H., Sun, Y., Genton, M. G., and Keyes., D. E. (2018), “Parallel approximation of the maximum likelihood estimation for the prediction of large-scale geostatistics simulations,” IEEE International Conference on Cluster Computing, 98-108.
[215] Arellano-Valle, R. B., Ferreira, C. S., and Genton, M. G. (2018), “Scale and shape mixtures of multivariate skew-normal distributions,” Journal of Multivariate Analysis, 166, 98-110.
[214] Castruccio, S., and Genton, M. G. (2018), “Principles for statistical inference on big spatiotemporal data from climate models,” Statistics and Probability Letters, Special Issue on The role of Statistics in the era of big data, 136, 92-96.
[213] Castruccio, S., Ombao, H., and Genton, M. G. (2018), “A scalable multi-resolution model for activation and brain connectivity in fMRI data,” Biometrics, 74, 823-833.
[212] Chen, W., Castruccio, S., Genton, M. G., and Crippa, P. (2018), “Current and future estimates of wind energy potential over Saudi Arabia,” Journal of Geophysical Research: Atmospheres, 123, 6443-6459.
[211] Dai, W., and Genton, M. G. (2018), “Functional boxplots for multivariate curves,” Stat, 7:e190.
[210] Dai, W., and Genton, M. G. (2018), “Multivariate functional data visualization and outlier detection,” Journal of Computational and Graphical Statistics, 27, 923-934.
[209] Dai, W., and Genton, M. G. (2018), “An outlyingness matrix for multivariate functional data classification,” Statistica Sinica, 28, 2435-2454.
[208] Genton, M. G., and Jeong, J. (2018), “discussion of Mission CO2ntrol: A statistical scientist’s role in remote sensing of atmospheric carbon dioxide,” by N. Cressie, Journal of the American Statistical Association, 113, 176-178.
[207] Genton, M. G., Keyes, D., and Turkiyyah, G. (2018), “Hierarchical decompositions for the computation of high-dimensional multivariate normal probabilities,” Journal of Computational and Graphical Statistics, 27, 268-277.
[206] Jeong, J., Castruccio, S., Crippa, P., and Genton, M. G. (2018), “Reducing storage of global wind ensembles with stochastic generators,” Annals of Applied Statistics, 12, 490-509.
[205] Krupskii, P., and Genton, M. G. (2018), “Linear factor copula models and their properties,” Scandinavian Journal of Statistics, 45, 861-878.
[204] Krupskii, P., Huser, R., and Genton, M. G. (2018), “Factor copula models for replicated spatial data,” Journal of the American Statistical Association, 113, 467-479.
[203] Krupskii, P., Joe, H., Lee, D., and Genton, M. G. (2018), “Extreme value limit of convolution of exponential and multivariate normal distribution: Link to the Huesler-Reiss distribution,” Journal of Multivariate Analysis, 163, 80-95.
[202] Vettori, S., Huser, R., and Genton, M. G. (2018), “A comparison of dependence function estimators in multivariate extremes,” Statistics and Computing, 58, 525-538.
[201] Yan, Y., and Genton, M. G. (2018), “Gaussian likelihood inference on data from trans-Gaussian random fields with Matern covariance function,” Environmetrics, 29, e2458.
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—2017—————————
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[200] Dutta, S., and Genton, M. G. (2017), “Depth-weighted robust multivariate regression with application to sparse data,” Canadian Journal of Statistics, 45, 164-184.
[199] Genton, M. G., and Hering, A. (2017), “discussion of Spatiotemporal models for skewed processes,” by A. Schmidt, K. Gon calves, P. Velozo, Environmetrics, 28, e2430.
[198] Ghosh, S., Dutta, S., and Genton, M. G. (2017), “A note on inconsistent families of discrete multivariate distributions,” Journal of Statistical Distributions and Applications, 4, 7.
[197] Jeong, J., Jun, M., and Genton, M. G. (2017), “Spherical process models for global spatial statistics,” Statistical Science, 32, 501-513.
[196] Krupskii, P., and Genton, M. G. (2017), “Factor copula models for data with spatio-temporal dependence,” Spatial Statistics, 22, 180-195.
[195] Xu, G., and Genton, M. G. (2017), “Tukey g-and-h random fields,” Journal of the American Statistical Association, 112, 1236-1249. (Supplementary Material)
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—2016—————————
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[194] Azzalini, A., Browne, R., Genton, M. G., and McNicholas, P. (2016), “On nomenclature for, and the relative merits of, two formulations of skew distributions,” Statistics and Probability Letters, 110, 201-206.
[193] Ben Taieb, S., Huser, R., Hyndman, R. J., and Genton, M. G. (2016), “Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression,” IEEE Transactions on Smart Grid, 7, 2448-2455.
[192] Castrillon-Candas, J., Genton, M. G., and Yokota, R. (2016), “Multi-level restricted maximum likelihood covariance estimation and kriging for large non-gridded spatial datasets,” Spatial Statistics, 18, 105-124.
[191] Castruccio, S., and Genton, M. G. (2016), “Compressing an ensemble with statistical models: An algorithm for global 3D spatio-temporal temperature,” Technometrics, 58, 319-328.
[190] Castruccio, S., Huser, R., and Genton, M. G. (2016), “High-order composite likelihood inference for max-stable distributions and processes,” Journal of Computational and Graphical Statistics, 25, 1212-1229. (Online Supplement)
[189] Cochran, J., Hardenstine, R., Braun, C., Skomal, G., Thorrold, S., Xu, K., Genton, M. G., and Berumen, M. (2016), “Population structure of a whale shark Rhincodon typus aggregation in the Red Sea,” Journal of Fish Biology, 89, 1570-1582.
[188] Dai, W., Tong, T., and Genton, M. G. (2016), “Optimal estimation of derivatives in nonparametric regression,” Journal of Machine Learning Research, 17(164), 1-25.
[187] Dong, K., Pang, H., Tong, T., and Genton, M. G. (2016), “Shrinkage-based diagonal Hotelling tests for high-dimensional small sample size data,” Journal of Multivariate Analysis, 143, 127-142.
[186] Genton, M. G., and Hall, P. (2016), “A tilting approach to ranking influence,” Journal of the Royal Statistical Society - Series B, 78, 77-97.
[185] Huser, R., Davison, A. C., and Genton, M. G. (2016), “Likelihood estimators for multivariate extremes,” Extremes, 19, 79-103.
[184] Huser, R., and Genton, M. G. (2016), “Non-stationary dependence structures for spatial extremes,” Journal of Agricultural, Biological and Environmental Statistics, 21, 470-491.
[183] Kim, H.-M., Maadooliat, M., Arellano-Valle, R., and Genton, M. G. (2016), “Skewed factor models using selection mechanisms,” Journal of Multivariate Analysis, 145, 162-177.
[182] Lee, M., Genton, M. G., and Jun, M. (2016), “Testing self-similarity through Lamperti transformations,” Journal of Agricultural, Biological and Environmental Statistics, 21, 426-447.
[181] Porcu, E., Bevilacqua, M., and Genton, M. G. (2016), “Spatio-temporal covariance and cross-covariance functions of great circle distance on the sphere,” Journal of the American Statistical Association, 111, 888-898. (Online Supplement)
[180] Prihartato, P. K., Irigoien, X., Genton, M. G., and Kaartvedt, S. (2016), “Global effects of moon phase on nocturnal acoustic scattering layers,” Marine Ecology Progress Series, 544, 65-75.
[179] Rubio, F. J., and Genton, M. G. (2016), “Bayesian linear regression with skew-symmetric error distributions, with applications to survival analysis,” Statistics in Medicine, 35, 2441-2454. (Supplementary Material)
[178] Xu, G., and Genton, M. G. (2016), “Tukey max-stable processes for spatial extremes,” Spatial Statistics, 18, 431-443.
[177] Zhelonkin, M., Genton, M. G., and Ronchetti, E. (2016), “Robust inference in sample selection models,” Journal of the Royal Statistical Society - Series B, 78, 805-827.
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—2015—————————
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[176] Azzalini, A., and Genton, M. G. (2015), “discussion of On families of distributions with shape parameters” by M. C. Jones, International Statistical Review, 83, 198-202.
[175] Castruccio, S., and Genton, M. G. (2015), “discussion of Comparing and selecting spatial predictors using local criteria” by Jonathan R. Bradley, Noel Cressie and Tao Shi, TEST, 24, 31-34.
[174] Chakraborty, A., De, S., Bowman, K. P., Sang, H., Genton, M. G., and Mallick, B. K. (2015), “An adaptive spatial model for precipitation data from multiple satellites over large regions,” Statistics and Computing, 25, 389-405.
[173] Genton, M. G., Castruccio, S., Crippa, P., Dutta, S., Huser, R., Sun, Y., and Vettori, S. (2015), “Visuanimations in statistics,” Stat, 4, 81-96.
[172] Genton, M. G., and Kleiber, W. (2015), “Cross-covariance functions for multivariate geostatistics (with discussion and rejoinder),” Statistical Science, 30, 147-163. (discussion 1,2,3,4,rejoinder)
[171] Genton, M. G., Padoan, S., and Sang, H. (2015), “Multivariate max-stable spatial processes,” Biometrika, 102, 215-230. (Supplementary Material)
[170] Goddard, S., Genton, M. G., Hering, M., and Sain, S. (2015), “Evaluating the impacts of climate change on diurnal wind power cycles using multiple regional climate models,” Environmetrics, 26, 192-201.
[169] Lee, G., Ding, Y., Genton, M. G., and Xie, L. (2015), “Power curve estimation with multivariate environmental factors for inland and off-shore wind farms,” Journal of the American Statistical Association, 110, 56-67.
[168] Lee, G., Ding, Y., Xie, L., and Genton, M. G. (2015), “A kernel plus method for quantifying wind turbine upgrades,” Wind Energy, 18, 1207-1219.
[167] Lee, M., Jun, M., and Genton, M. G. (2015), “Validation of CMIP5 multi-model ensembles through the smoothness of climate variables,” Tellus A, 67, 23880.
[166] Militino, A., Ugarte, M., Goicoa, T., and Genton, M. G. (2015), “Interpolation of daily rainfall using spatiotemporal models and clustering,” International Journal of Climatology, 35, 1453-1464.
[165] Ngo, D., Sun, Y., Genton, M. G.,Wu, J., Cramer, S. C., and Ombao, H. (2015), “An exploratory data analysis of electroencephalograms using the functional boxplot approach,” Frontiers in Neuroscience, 9, Article 282, 1-18.
[164] Razafindrakoto, H., Mai, P. M., Zhang, l., Genton, M. G., and Thingbaijam, K. (2015), “Quantifying variability in earthquake rupture models using multidimensional scaling: Stability analysis and application to the 2011 Tohoku earthquake,” Geophysical Journal International, 202, 17-40.
[163] Roh, S., Jun, M., Szunyogh, I., and Genton, M. G. (2015), “Multivariate localization methods for ensemble Kalman filtering,” Nonlinear Processes in Geophysics, 22, 723-735.
[162] Sun, Y., Bowman, K., Genton, M. G., and Tokay, A. (2015), “A Matern model of the spatial covariance structure of point rain rates,” Stochastic Environmental Research and Risk Assessment, 29, 411-416.
[161] Xu, G., and Genton, M. G. (2015), “Efficient maximum approximated likelihood inference for Tukey’s g-and-h distribution,” Computational Statistics and Data Analysis, 91, 78-91. (Supplementary Material)
[160] Xu, G., Liang, F., and Genton, M. G. (2015), “A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets,” Statistica Sinica, 25, 61-79.
[159] Yan, Y., and Genton, M. G. (2015), “discussion of Multivariate functional outlier detection” by M. Hubert, P. Rousseeuw, and P. Segaert, Statistical Methods and Applications, 24, 245-251.
[158] Zenger, K., Dutta, S., Wol , H., Genton, M. G., and Kraus, B. (2015), “In vitro structure-toxicity relationship of chalcones in human hepatic stellate cells,” Toxicology, 336, 26-33.
[157] Zhang, l., Mai, P. M., Thingbaijam, K., Raza ndrakoto, H., and Genton, M. G. (2015), “Analysing earthquake slip models with the spatial prediction comparison test,” Geophysical Journal International, 200, 185-198.
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—2014—————————
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[156] Castruccio, S., and Genton, M. G. (2014), “Beyond axial symmetry: An improved class of models for global data,” Stat, 3, 48-55.
[155] Dao, A., and Genton, M. G. (2014), “A Monte Carlo goodness-of-fit test for parametric models describing spatial point patterns,” Journal of Computational and Graphical Statistics, 23, 497-517.
[154] Dutta, S., and Genton, M. G. (2014), “A non-Gaussian multivariate distribution with all lower- dimensional Gaussians and related families,” Journal of Multivariate Analysis, 132, 82-93.
[153] Genton, M. G., Johnson, C., Potter, K., Stenchikov, G., and Sun, Y. (2014), “Surface boxplots,” Stat, 3, 1-11. (code)
[152] Kim, H.-M., Ryu, D., Mallick, B., and Genton, M. G. (2014), “Mixtures of skewed Kalman filters,” Journal of Multivariate Analysis*, 123, 228-251.
[151] Lopez-Pintado, S., Sun, Y., Lin, J. K., and Genton, M. G. (2014), “Simplicial band depth for multivariate functional data,” Advances in Data Analysis and Classification, 8, 321-338.
[150] Sang, H., and Genton, M. G. (2014), “Tapered composite likelihood for spatial max-stable models,” Spatial Statistics, 8, 86-103.
[149] Xie, L., Gu, Y., Zhu, X., and Genton, M. G. (2014), “Short-term spatio-temporal wind forecast for enhanced power system dispatch,” IEEE Transactions on Smart Grid, 5, 511-520.
[148] Zhu, X., Bowman, K., and Genton, M. G. (2014), “Incorporating geostrophic wind information for improved space-time short-term wind speed forecasting,” Annals of Applied Statistics, 8, 1782-1799.
[147] Zhu, X., Genton, M. G., Gu, Y., and Xie, L. (2014), “Space-time wind speed forecasting for improved power system dispatch (with discussion),” TEST, 23, 1-25. (discussion 1,2,3,4,rejoinder)
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—2013—————————
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[146] Arellano-Valle, R. B., Contreras-Reyes, J., and Genton, M. G. (2013), “Shannon entropy and mutual information for multivariate skew-elliptical distributions,” Scandinavian Journal of Statistics, 40, 42-62. (Corrigendum)
[145] Branco, M. D., Genton, M. G., and Liseo, B. (2013), “Objective Bayesian analysis of skew-t distributions,” Scandinavian Journal of Statistics, 40, 63-85.
[144] El Ghouch, A., Genton, M. G., and Bouezmarni, T. (2013), “Measuring the discrepancy of a parametric model via local polynomial smoothing,” Scandinavian Journal of Statistics, 40, 455-470.
[143] Kleiber, W., and Genton, M. G. (2013), “Spatially varying cross-correlation coefficients in the presence of nugget effects,” Biometrika, 100, 213-220.
[142] Li, B., and Genton, M. G. (2013), “Nonparametric identification of copula structures,” Journal of the American Statistical Association, 108, 666-675.
[141] Ma, Y., Kim, M.-J., and Genton, M. G. (2013), “Semiparametric efficient and robust estimation of an unknown symmetric population under arbitrary selection bias,” Journal of the American Statistical Association, 108, 1090-1104.
[140] Potgieter, N., and Genton, M. G. (2013), “Characteristic function based semiparametric inference for skew-symmetric models,” Scandinavian Journal of Statistics, 40, 471-490.
[139] Roh, S., Genton, M. G., Jun, M., Szunyogh, I., and Hoteit, I. (2013), “Observation quality control with a robust ensemble Kalman filter,” Monthly Weather Review, 141, 4414-4428.
[138] Sun, Y., Hart, J. D., and Genton, M. G. (2013), “Improved nonparametric inference for multiple correlated periodic sequences,” Stat, 2, 197-210.
[137] Zollanvari, A., and Genton, M. G. (2013), “On Kolmogorov asymptotics of misclassification error rates in linear discriminant analysis,” Sankhya A, 75, 300-326.
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—2012—————————
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[136] Apanasovich, T. V., Genton, M. G., and Sun, Y. (2012), “A valid Matern class of cross-covariance functions for multivariate random fields with any number of components,” Journal of the American Statistical Association, 107, 180-193.
[135] Bliznyuk, N., Carroll, R. J., Genton, M. G., and Wang, Y. (2012), “Variogram estimation in the presence of trend,” Statistics and Its Interface, 5, 159-168.
[134] Furrer, R., Genton, M. G., and Nychka, D. (2012), “Erratum and Addendum to Covariance Tapering for Interpolation of Large Spatial Datasets published in the Journal of Computational and Graphical Statistics, 15, 502–523,” Journal of Computational and Graphical Statistics, 21, 823-824.
[133] Genton, M. G., Kim, M., and Ma, Y. (2012), “Semiparametric location estimation under non-random sampling,” Stat, 1, 1-11.
[132] Genton, M. G., and Zhang, H. (2012), “Identifiability problems in some non-Gaussian spatial random fields,” Chilean Journal of Statistics, 3, 61-69.
[131] Irincheeva, I., Cantoni, E., and Genton, M. G. (2012), “A non-Gaussian spatial generalized linear latent variable model,” Journal of Agricultural, Biological, and Environmental Statistics, 17, 332-353.
[130] Irincheeva, I., Cantoni, E., and Genton, M. G. (2012), “Generalized linear latent variable models with flexible distribution of latent variables,” Scandinavian Journal of Statistics, 39, 663-680.
[129] Jun, M., and Genton, M. G. (2012), “A test for stationarity of spatio-temporal random fields on planar and spherical,” Statistica Sinica, 22, 1737-1764.
[128] Mahajan, S., North, G. R., Saravanan, R., and Genton, M. G. (2012), “Statistical significance of trends in monthly heavy precipitation over the US,” Climate Dynamics, 38, 1375-1387.
[127] Marchenko, Y., and Genton, M. G. (2012), “A Heckman selection-t model,” Journal of the American Statistical Association, 107, 304-317.
[126] Sun, Y., and Genton, M. G. (2012), “Functional median polish,” Journal of Agricultural, Biological, and Environmental Statistics, 17, 354-376.
[125] Sun, Y., and Genton, M. G. (2012), “Adjusted functional boxplots for spatio-temporal data visualization and outlier detection,” Environmetrics, 23, 54-64.
[124] Sun, Y., Genton, M. G., and Nychka, D. (2012), “Exact fast computation of band depth for large functional datasets: How quickly can one million curves be ranked?,” Stat, 1, 68-74.
[123] Sun, Y., Hart, J. D., and Genton, M. G. (2012), “Nonparametric inference for periodic sequences,” Technometrics, 54, 83-96.
[122] Sun, Y., Li, B., and Genton, M. G. (2012), “Geostatistics for large datasets,” in Space-Time Processes and Challenges Related to Environmental Problems, E. Porcu, J. M. Montero, M. Schlather (eds), Springer, 207, Chapter 3, 55-77.
[121] Zhelonkin, M., Genton, M. G., and Ronchetti, E. (2012), “On the robustness of two-stage estimators,” Statistics and Probability Letters, 82, 726-732.
[120] Zhu, X., and Genton, M. G. (2012), “Short-term wind speed forecasting for power system operations,” International Statistical Review, 80, 2-23.
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—2011—————————
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[119] Furrer, R., and Genton, M. G. (2011), “Aggregation-cokriging for highly-multivariate spatial data,” Biometrika, 98, 615-631.
[118] Genton, M. G. (2011), “book review of Computation of Multivariate Normal and t Probabilities,” by A. Genz and F. Bretz, Springer, 2009, Journal of the American Statistical Association, 106, 1641.
[117] Genton, M. G., Ma, Y., Sang, H. (2011), “On the likelihood function of Gaussian max-stable processes,” Biometrika, 98, 481-488.
[116] Hering, A., and Genton, M. G. (2011), “Comparing spatial predictions,” Technometrics, 53, 414-425.
[115] Jun, M., Szunyogh, I., Genton, M. G., Zhang, F., and Bishop, C. (2011), “A statistical investigation of the sensitivity of ensemble based Kalman filters to covariance filtering,” Monthly Weather Review, 139, 3036-3051.
[114] Kim, H,-M., and Genton, M. G. (2011), “Characteristic functions of scale mixtures of multivariate skew-normal distributions,” Journal of Multivariate Analysis, 102, 1105-1117.
[113] Li, B., and Genton, M. G. (2011), “discussion of An explicit link between Gaussian fields and Gaussian Markov random fields: The stochastic partial differential equation approach” by Haavard Rue, Finn Lindgren and Johan Lindstrom, Journal of the Royal Statistical Society - Series B, 73, 480-481.
[112] Ma, Y., Genton, M. G., and Parzen, E. (2011), “Asymptotic properties of sample quantiles of discrete distributions,” Annals of the Institute of Statistical Mathematics, 63, 221-237.
[111] North, G., Wang, J., and Genton, M. G. (2011), “Correlation models for temperature fields,” Journal of Climate, 24, 5850-5862.
[110] Sun, Y., and Genton, M. G. (2011), “Functional boxplots,” Journal of Computational and Graphical Statistics, 20, 316-334.
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—2010—————————
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[109] Ammann, C. M., Genton, M. G., and Li, B. (2010), “Correcting for signal attenuation from noisy proxy data in climate reconstructions,” Climate of the Past, 6, 273-279.
[108] Apanasovich, T. V., and Genton, M. G. (2010), “Cross-covariance functions for multivariate random fields based on latent dimensions,” Biometrika, 97, 15-30.
[107] Arellano-Valle, R. B., and Genton, M. G. (2010), “Multivariate unified skew-elliptical distributions,” Chilean Journal of Statistics, Special issue “Tribute to Pilar Loreto Iglesias Zuazola,” 1, 17-33.
[106] Arellano-Valle, R. B., and Genton, M. G. (2010), “Multivariate extended skew-t distributions and related families,” Metron, Special issue on “Skew-symmetric and flexible distributions,” 68, 281-314.
[105] Arellano-Valle, R. B., and Genton, M. G. (2010), “An invariance property of quadratic forms in random vectors with a selection distribution, with application to sample variogram and covariogram estimators,” Annals of the Institute of Statistical Mathematics, 62, 363-381.
[104] Azzalini, A., Genton, M. G., and Scarpa, B. (2010), “Invariance-based estimating equations for skew-symmetric distributions,” Metron, Special issue on “Skew-symmetric and flexible distributions,” 68, 353-377.
[103] Genton, M. G., and Ruiz-Gazen, A. (2010), “Visualizing influential observations in dependent data,” Journal of Computational and Graphical Statistics, 19, 808-825.
[102] Hering, A. M., and Genton, M. G. (2010), “Powering up with space-time wind forecasting,” Journal of the American Statistical Association, 105, 92-104.
[101] Lee, S., Genton, M. G., and Arellano-Valle, R. B. (2010), “Perturbation of numerical confidential data via skew-t distributions,” Management Science, 56, 318-333.
[100] Ma, Y., and Genton, M. G. (2010), “Explicit estimating equations for semiparametric generalized linear latent variable models,” Journal of the Royal Statistical Society - Series B, 72, 475-495.
[99] Marchenko, Y. V., and Genton, M. G. (2010), “A suite of commands for fitting the skew-normal and skew-t models,” The Stata Journal, 10, 507-539.
[98] Marchenko, Y. V., and Genton, M. G. (2010), “Multivariate log-skew-elliptical distributions with applications to precipitation data,” Environmetrics, 21, 318-340
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—2009—————————
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[97] Arellano-Valle, R. B., Genton, M. G., and Loschi, R. H. (2009), “Shape mixtures of multivariate skew-normal distributions,” Journal of Multivariate Analysis, 100, 91-101.
[96] El Ghouch, A., and Genton, M. G. (2009), “Local polynomial quantile regression with para- metric features,” Journal of the American Statistical Association, 104, 1416-1429.
[95] Hering, A. S., Bell, C. L., and Genton, M. G. (2009), “Modeling spatio-temporal wildfire ignition point patterns,” Environmental and Ecological Statistics, Special Issue on Statistics for Wildfire Processes, 16, 225-250.
[94] Li, B., Murthi, A., Bowman, K., North, G., Genton, M. G., and Sherman, M. (2009), “Statistical tests of Taylor’s hypothesis: An application to precipitation fields,” Journal of Hydrometeorology, 10, 254-265.
[93] Li, Y., and Genton, M. G. (2009), “Single-index additive vector autoregressive time series models,” Scandinavian Journal of Statistics, 36, 369-388.
[92] Park, J. W., Genton, M. G., and Ghosh, S. K. (2009), “Nonparametric autocovariance estimation from censored time series by Gaussian imputation,” Journal of Nonparametric Statistics, 21, 241-259.
[91] Sills, E. S., Genton, M. G., Walsh, A. P. H., and Wehbe, S. A. (2009), “Who’s asking? Patients may under-report postoperative pain scores to nurses (or over-report to surgeons) following surgery of the female reproductive tract,” Archives of Gynecology and Obstetrics, 5, 771-774.
[90] Sills, E. S., Murray, G. U., Genton, M. G., Walsh, D. J., Coull, G. D., and Walsh, A. P. H. (2009), “Clinical features and reproductive outcomes for embryos undergoing dual freeze-thaw sequences followed by blastocyst transfer: Critique of fourteen consecutive cases in IVF,” Fertility and Sterility, 91, 1568-1570.
[89] Wang, Q., Stefanski, L. A., Genton, M. G., and Boos, D. D. (2009), “Robust time series analysis via measurement error modeling,” Statistica Sinica, 19, 1263-1280.
[88] Wu, Y., Genton, M. G., and Stefanski, L. A. (2009), “A comparison of node-splitting rules in recursive partitioning analysis of multivariate quantitative structure activity data,” Statistics in Biopharmaceutical Research, 1, 119-130.
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—2008—————————
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[87] Arellano-Valle, R. B., Castro, L. M., Genton, M. G., and Gomez, H. W. (2008), “Bayesian inference for shape mixtures of skewed distributions, with application to regression analysis,” Bayesian Analysis, 3, 513-540.
[86] Arellano-Valle, R. B., and Genton, M. G. (2008), “On the exact distribution of the maximum of absolutely continuous dependent random variables,” Statistics and Probability Letters, 78, 27-35.
[85] Azzalini, A., and Genton, M. G. (2008), “Robust likelihood methods based on the skew-t and related distributions,” International Statistical Review, 76, 106-129.
[84] Butry, D., Gumpertz, M. L., and Genton, M. G. (2008), “The production of large and small wildfires,” in Holmes, T. P., Prestemon, J. P., and Abt, K. L. (eds.), The Economics of Forest Disturbances: Wildfires, Storms, and Invasive Species, Springer: Dordrecht, The Netherlands, Chapter 5, 79-106.
[83] Genton, M. G., and Koul, H. L. (2008), “Minimum distance inference in unilateral autoregressive lattice processes,” Statistica Sinica, 18, 617-631.
[82] Genton, M. G., and Ronchetti, E. (2008), “Robust prediction of beta,” in Kontoghiorghes, E. J., Rustem, B. and Winker, P. (eds.), Computational Methods in Financial Engineering, Essays in Honour of Manfred Gilli, Springer, 147-161.
[81] Li, B., Genton, M. G., and Sherman, M. (2008), “On the asymptotic joint distribution of sample space-time covariance estimators,” Bernoulli, 14, 228-248.
[80] Li, B., Genton, M. G., and Sherman, M. (2008), “Testing the covariance structure of multivariate random fields,” Biometrika, 95, 813-829.
[79] Sills, E. S., Murray, G. U., Genton, M. G., Walsh, D. J., Coull, G. D., and Walsh, A. P. H. (2008), “Ovarian hyperstimulation syndrome and prophylactic human embryo cryopreservation: Analysis of reproductive outcome following thawed embryo transfer,” Journal of Ovarian Research, 1:7, 1-6.
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—2007—————————
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[78] Arellano-Valle, R. B., and Genton, M. G. (2007), “On the exact distribution of linear combinations of order statistics from dependent random variables,” Journal of Multivariate Analysis, 98, 1876-1894. Corrigendum
[77] Azzalini, A., and Genton, M. G. (2007), “On Gauss’s characterization of the normal distribution,” Bernoulli, 13, 169-174.
[76] Chang, S.-M., and Genton, M. G. (2007), “Extreme value distributions for the skew-symmetric family of distributions,” Communications in Statistics: Theory and Methods, Special issue on skew-elliptical distributions and their applications, 36, 1705-1717.
[75] Genton, M. G. (2007), “Separabale approximations of space-time covariance matrices,” Environmetrics, Special Issue for METMA3, 18, 681-695.
[74] Genton, M. G., and Hall, P. (2007), “Statistical inference for evolving periodic functions,” Journal of the Royal Statistical Society - Series B, 69, 643-657.
[73] Genton, M. G., and Hering, A. S. (2007), “Blowing in the wind,” Significance, 4, 11-14.
[72] Genton, M. G., Perrin, O., and Taqqu, M. S. (2007), “Self-similarity and Lamperti transformation for random fields,” Stochastic Models, 23, 397-411.
[71] Gneiting, T., Genton, M. G., and Guttorp, P. (2007), “Geostatistical space-time models, stationarity, separability and full symmetry,” in Finkenstaedt, B., Held, L. and Isham, V. (eds), Statistics of Spatio-Temporal Systems, Chapman & Hall / CRC Press, Monograph in Statistics and Applied Probability, 151-175.
[70] Li, B., Genton, M. G., and Sherman, M. (2007), “A nonparametric assessment of properties of space-time covariance functions,” Journal of the American Statistical Association, 102, 736-744.
[69] Park, J. W., Genton, M. G., and Ghosh, S. K. (2007), “Censored time series analysis with autoregressive moving average models,” The Canadian Journal of Statistics, 35, 151-168.
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—2006—————————
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[68] Arellano-Valle, R. B., Branco, M. D., and Genton, M. G. (2006), “A unified view on skewed distributions arising from selections,” The Canadian Journal of Statistics, 34, 581-601.
[67] De Luca, G., Genton, M. G., and Loperfido, N. (2006), “A multivariate Skew-GARCH model,” in Advances in Econometrics: Econometric Analysis of Economic and Financial Time Series, Special volume in honor of R. Engle and C. Granger, the 2003 winners of the Nobel Prize in Economics, D. Terrell (ed.), Elsevier, 20A, 33-57.
[66] Field, C., and Genton, M. G. (2006), “The multivariate g-and-h distribution,” Technometrics, 48, 104-111.
[65] Furrer, R., Genton, M. G., and Nychka, D. (2006), “Covariance tapering for interpolation of large spatial datasets,” Journal of Computational and Graphical Statistics, 15, 502-523.
[64] Genton, M. G., Butry, D. T., Gumpertz, M. L., and Prestemon, J. P. (2006), “Spatio-temporal analysis of wildfire ignitions in the St. Johns river water management district,” International Journal of Wildland Fire, 15, 87-97.
[63] Genton, M. G., Ma, Y., and Parzen, E. (2006), “discussion of Sur une limitation tres generale de la dispersion de la mediane,” by M. Frechet, 1940. Journal de la Societe Francaise de Statistique, 147, 51-60.
[62] Gneiting, T., Larson, K., Westrick, K., Genton, M. G., and Aldrich, E. (2006), “Calibrated probabilistic forecasting at the Stateline wind energy center: The regime-switching space-time method,” Journal of the American Statistical Association, 101, 968-979.
[61] Mitchell, M., Genton, M. G., and Gumpertz, M. (2006), “A likelihood ratio test for separability of covariances,” Journal of Multivariate Analysis, 97, 1025-1043.
[60] Wang, J., and Genton, M. G. (2006), “The multivariate skew-slash distribution,” Journal of Statistical Planning and Inference, 136, 209-220.
[59] Wu, Y., Genton, M. G., and Stefanski, L. A. (2006), “A multivariate two-sample mean test for small sample size and missing data,” Biometrics, 62, 877-885. (Yujun Wu received the 2005 David P. Byar young investigator award from the ASA Biometrics Society for this paper)
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—2005—————————
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[58] Arellano-Valle, R. B., and Genton, M. G. (2005), “On fundamental skew distributions,” Journal of Multivariate Analysis, 96, 93-116.
[57] de Luna, X., and Genton, M. G. (2005), “Predictive spatio-temporal models for spatially sparse environmental data,” Statistica Sinica, special issue in honor of George Tiao’s retirement, 15, 547-568.
[56] Genton, M. G. (2005), “discussion of The skew-normal distribution and related multivariate families” by A. Azzalini, Scandinavian Journal of Statistics, 32, 189-198.
[55] Genton, M. G., and Loperfido, N. (2005), “Generalized skew-elliptical distributions and their quadratic forms,” Annals of the Institute of Statistical Mathematics, 57, 389-401.
[54] Genton, M. G., and Lucas, A. (2005), “discussion of Breakdown and groups” by L. Davies and U. Gather, Annals of Statistics, 33, 988-993.
[53] Ma, Y., Genton, M. G., and Tsiatis, A. A. (2005), “Locally efficient semiparametric estimators for generalized skew-elliptical distributions,” Journal of the American Statistical Association, 100, 980-989.
[52] Mitchell, M., Genton, M. G., and Gumpertz, M. (2005), “Testing for separability of space-time covariances,” Environmetrics, 16, 819-831.
[51] Naveau, P., Genton, M. G., and Shen, X. (2005), “A skewed Kalman filter,” Journal of Multivariate Analysis, 94, 382-400.
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—2004—————————
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[50] Baloch, S. H., Krim, H., and Genton, M. G. (2004), “Shape representation with flexible skew- symmetric distributions,” in Skew-Elliptical Distributions and Their Applications: A Journey Beyond Normality, Genton, M. G., Ed., Chapman & Hall / CRC, Boca Raton, FL, pp. 291-308.
[49] de Luna, X., and Genton, M. G. (2005), “Spatio-temporal autoregressive models for US unemployment rate,” in Advances in Econometrics: Spatial and Spatiotemporal Econometrics, J. P. Lesage, R. K. Pace (eds), Elsevier, 18, 283-298.
[48] Eyer, L., and Genton, M. G. (2004), “An astronomical distance determination method using regression with skew-normal errors,” in Skew-Elliptical Distributions and Their Applications: A Journey Beyond Normality, Genton, M. G., Ed., Chapman & Hall / CRC, Boca Raton, FL, pp. 309-319.
[47] Genton, M. G. (2004), “Skew-symmetric and generalized skew-elliptical distributions,” in Skew- Elliptical Distributions and Their Applications: A Journey Beyond Normality, Genton, M. G., Ed., Chapman & Hall / CRC, Boca Raton, FL, pp. 81-100.
[46] Genton, M. G., and Perrin, O. (2004), “On a time deformation reducing nonstationary stochastic processes to local stationarity,” Journal of Applied Probability, 41, 236-249.
[45] Genton, M. G., and Thompson, K. (2004), “Skew-elliptical time series with application to flooding risk,” in Time Series Analysis and Applications to Geophysical Systems, D. R. Brillinger, E. A. Robinson, F. P. Schoenberg (eds), IMA Volume in Mathematics and its Applications 139, Springer, 169-186.
[44] Gorsich, D. J., and Genton, M. G. (2004), “On the discretization of nonparametric covariogram estimators,” Statistics and Computing, 14, 99-108.
[43] Ma, Y., and Genton, M. G. (2004), “A flexible class of skew-symmetric distributions,” Scandinavian Journal of Statistics, 31, 459-468.
[42] Ma, Y., Genton, M. G., and Davidian, M. (2004), “Linear mixed effects models with flexible generalized skew-elliptical random effects,” in Skew-Elliptical Distributions and Their Applications: A Journey Beyond Normality, Genton, M. G., Ed., Chapman & Hall / CRC, Boca Raton, FL, pp. 339-358.
[41] Naveau, P., Genton, M. G., and Ammann, C. (2004), “Time series analysis with a skewed Kalman filter,” in Skew-Elliptical Distributions and Their Applications: A Journey Beyond Normality, Genton, M. G., Ed., Chapman & Hall / CRC, Boca Raton, FL, pp. 259-278.
[40] Wang, J., Boyer, J., and Genton, M. G. (2004), “A skew-symmetric representation of multivariate distributions,” Statistica Sinica, 14, 1259-1270.
[39] Wang, J., Boyer, J., and Genton, M. G. (2004), “A note on an equivalence between chi-square and generalized skew-normal distributions,” Statistics and Probability Letters, 66, 395-398.
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—2003—————————
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[38] Ehm, W., Genton, M. G., and Gneiting, T. (2003), “Stationary covariances associated with exponentially convex functions,” Bernoulli, 9, 1-9. Acknowledgement of priority.
[37] Genton, M. G. (2003), “Breakdown-point for spatially and temporally correlated observations,” in Developments in Robust Statistics, International Conference on Robust Statistics 2001, R. Dutter, P. Filzmoser, U. Gather and P. J. Rousseeuw (eds), Springer, 148-159.
[36] Genton, M. G., and Furrer, R., (2003), “Analysis of rainfall data by robust spatial statistics using S+SpatialStats,” in Mapping radioactivity in the environment - Spatial Interpolation Comparison 97, G. Dubois, J. Malczewski, M. De Cort (eds), 118-129.
[35] Genton, M. G., and Furrer, R., (2003), “Analysis of rainfall data by simple good sense: Is Spatial Statistics worth the trouble?,” in Mapping radioactivity in the environment - Spatial Interpolation Comparison 97, G. Dubois, J. Malczewski, M. De Cort (eds), 45-50.
[34] Genton, M. G., and Lucas, A. (2003), “Comprehensive definitions of breakdown point for independent and dependent observations,” Journal of the Royal Statistical Society - Series B, 65, 81-94.
[33] Genton, M. G., and Ronchetti, E. (2003), “Robust indirect inference,” Journal of the American Statistical Association, 98, 67-76.
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—2002—————————
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[32] de Luna, X., and Genton, M. G. (2002), “Simulation-based inference for simultaneous processes on regular lattices,” Statistics and Computing, 12, 125-134.
[31] Genton, M. G., and Gorsich, D. J. (2002), “Nonparametric variogram and covariogram estimation with Fourier-Bessel matrices,” Computational Statistics and Data Analysis, Special issue on Matrix Computations and Statistics, 41, 47-57.
[30] Gorsich, D. J., Genton, M. G., and Strang, G. (2002), “Eigenstructures of spatial design matrices,” Journal of Multivariate Analysis, 80, 138-165.
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—2001—————————
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[29] de Luna, X., and Genton, M. G. (2001), “Robust simulation-based estimation of ARMA models,” Journal of Computational and Graphical Statistics, 10, 370-387.
[28] Genton, M. G. (2001), “Classes of kernels for machine learning: A statistics perspective,” Journal of Machine Learning Research, Special issue on Kernel Methods, 2, 299-312.
[27] Genton, M. G. (2001), “The change-of-variance function: A tool to explore the effects of dependencies in spatial statistics,” Journal of Statistical Planning and Inference, 98, 191-209.
[26] Genton, M. G. (2001), “Robustness problems in the analysis of spatial data,” in Spatial Statistics: Methodological Aspects and Some Applications, M. Moore (ed.), Springer Lecture Notes in Statistics, 159, 21-37.
[25] Genton, M. G., He, L., and Liu, X. (2001), “Moments of skew-normal random vectors and their quadratic forms,” Statistics and Probability Letters, 51, 319-325.
[24] Ma, Y., and Genton, M. G. (2001), “Highly robust estimation of dispersion matrices,” Journal of Multivariate Analysis, 78, 11-36.
[23] Sills, E. S., Genton, M. G., Perloe, M., Schattman, G. L., Bralley, J. A., and Tucker, M. J. (2001), “Plasma homocysteine, fasting insulin, and androgen patterns among women with polycystic ovaries and infertility,” Journal of Obstetrics and Gynaecology Research, 27, 163-168.
[22] Sills, E. S., Perloe, M., Tucker, M. J., Kaplan, C. R., Genton, M. G., and Schattman, G. L. (2001), “Diagnostic and treatment characteristics of polycystic ovary syndrome: Descriptive measurements of patient perception and awareness from 657 confidential self-reports,” BMC Women’s Health, 1, 1-5.
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—2000—————————
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[21] Genton, M. G. (2000), “The correlation structure of Matheron’s classical variogram estimator under elliptically contoured distributions,” Mathematical Geology, 32, 127-137.
[20] Genton, M. G., and de Luna, X. (2000), “Robust simulation-based estimation,” Statistics and Probability Letters, 48, 253-259.
[19] Gorsich, D. J., and Genton, M. G. (2000), “Variogram model selection via nonparametric derivative estimation,” Mathematical Geology, 32, 249-270.
[18] Ma, Y., and Genton, M. G. (2000), “Highly robust estimation of the autocovariance function,” Journal of Time Series Analysis, 21, 663-684.
[17] Sills, E. S., Genton, M. G., Perloe, M., Schattman, G. L., Bralley, J. A., and Tucker, M. J. (2000), “Patient perception and awareness regarding diagnosis and treatment of polycystic ovary syndrome (PCOS) as measured by confidential self-reports,” Fertility and Sterility, 74, S190-S191.
[16] Sills, E. S., Genton, M. G., Perloe, M., Schattman, G. L., Bralley, J. A., and Tucker, M. J. (2000), “Plasminogen activator inhibitor-1 levels, prothrombin G20210A and methyltetrahydro-folate reductase C677T gene polymorphism frequencies, and reproductive history: Correlations with ultrasonographic ovarian morphology,” Fertility and Sterility, 74, S13-S14.
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—1999—————————
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[15] Eyer, L., and Genton, M. G. (1999), “Characterization of variable stars by robust wave variograms: An application to Hipparcos mission,” Astronomy and Astrophysics Supplement Series, 136, 421-428.
[14] Furrer, R., and Genton, M. G. (1999), “Robust spatial data analysis of Lake Geneva sediments with S+SpatialStats,” Systems Research and Information Systems, special issue on Spatial Data Analysis and Modeling, 8, 257-272.
[13] Genton, M. G. (1999), “The correlation structure of the sample autocovariance function for a particular class of time series with elliptically contoured distribution,” Statistics and Probability Letters, 41, 131-137.
[12] Genton, M. G., and de Luna, X. (1999), “Indirect inference for spatio-temporal autoregression models,” in proceedings of Spatial-temporal modeling and its application, Leeds, UK, 61-64.
[11] Genton, M. G., and Ma, Y. (1999), “Robustness properties of dispersion estimators,” Statistics and Probability Letters, 44, 343-350.
[10] Gorsich, D. J., Karlsen, R. E., Gerhart, G. R., and Genton, M. G. (1999), “Target versus back-ground characterization: Second generation wavelets and support vector machines,” in proceedings of SPIE, 12p.
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—1998—————————
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[9] Genton, M. G. (1998), “Highly robust variogram estimation,” Mathematical Geology, 30, 213- 221.
[8] Genton, M. G. (1998), “Variogram fitting by generalized least squares using an explicit formula for the covariance structure,” Mathematical Geology, 30, 323-345.
[7] Genton, M. G. (1998), “Spatial breakdown point of variogram estimators,” Mathematical Geology, 30, 853-871.
[6] Genton, M. G. (1998), “Asymptotic variance of M-estimators for dependent Gaussian random variables,” Statistics and Probability Letters, 38, 255-261.
[5] Genton, M. G., and Furrer, R. (1998), “Analysis of rainfall data by simple good sense: Is spatial statistics worth the trouble?,” Journal of Geographic Information and Decision Analysis, 2, 11-17.
[4] Genton, M. G., and Furrer, R. (1998), “Analysis of rainfall data in Switzerland by robust spatial statistics using S+SpatialStats,” Journal of Geographic Information and Decision Analysis, 2, 126-136.
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—1997—————————
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[3] Benvenuti J.-F., Rakotomanana L., Leyvraz P. F., Pioletti D., Heegaard J. H., and Genton, M. G. (1997), “Displacements of the tibial tuberosity: Effects of the surgical parameters,” Clinical Orthopaedics and Related Research, 343, 224-234.
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—1996—————————
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[2] Genton, M. G. (1996), “Robust variogram estimation and fitting in geostatistics,” PhD thesis no. 1595, EPFL-DMA.
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—1995—————————
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[1] Genton, M. G., and Rousseeuw, P. J. (1995), “The change-of-variance function of M-estimators of scale under general contamination,” Journal of Computational and Applied Mathematics, 64, 69-80.