Book

 

Genton, M. G. (2004), Skew-Elliptical Distributions and Their Applications: A Journey Beyond Normality, Edited Volume, Chapman & Hall / CRC, Boca Raton, FL, 416 pp.​


Surface Boxplots (zipped file)

 



 2024

 

[310] Hu, Z., Tong, T., and Genton, M. G. (2024), "A pairwise Hotelling method for testing high-dimensional mean vectors," Statistica Sinica, 34, 229-256.

[309] Mondal, S., Arellano-Valle, R. B., and Genton, M. G. (2024), "The multivariate modified skew-normal distribution," Statistical Papers, to appear.

[308] Mondal, S., and Genton, M. G. (2024), "A multivariate skew-normal-Tukey-h distribution," Journal of Multivariate Analysis, 200:105260.

[307] Pan, Q., Abdulah, S., Genton, M. G., Keyes, D. E., Keyes, D. E.Ltaief, H., and Sun, Y.Sun, Y., (2024), "GPU-accelerated Vecchia approximations of Gaussian processes for geospatial data using batched matrix computations," ISC High Performance, to appear.

[306] Qu, Z., Dai, W., and Genton, M. G. (2024), "Robust two-layer partition clustering of sparse multivariate functional data," Econometrics and Statistics, to appear.

[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, to appear.

 


 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. 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. (2023), "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. H., 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. (Disc1, Disc2, Disc3, Disc4, Disc5, 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 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 Lilo, 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 non-identifiability 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

 

[287] Abdulah, S., Castruccio, S., Genton, M. G., and Genton, M. G.Genton, M. G.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 data sets," 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," "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. E., and Turkiyyah, G. (2022), "tlrmvnmvt: Computing high-dimensional multivariate normal and Student-t probabilities with low-rank methods in R," Journal of Statistical Software, 101:4.​

[281] Cao, J., Guiness, 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 spatio-temporal 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," IEEE 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. E., and Turkiyyah, G. (2021), "Sum of Kronecker products representation and its Choleski 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. E., and Turkiyyah, G. (2021), "Exploiting low rank covariance structures for computing high-dimensional normal and Student-t probabilities," Statistics and Computing, 31:2.​

[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 temporal solar 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, 1579-1690.

[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. L., 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.


 

 2020

 

[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]