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)





[] .




[284] 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.

[283] 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, to appear.

[282] 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, to appear.​

[281] 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.​

[280] Chen, W., and Genton, M. G. (2022), "Are you all normal? It depends!," International Statistical Review, to appear. 

[279] 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.

[278] 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.

[277] Hu, Z., Tong, T., and Genton, M. G. (2022), "A pairwise Hotelling method for testing high-dimensional mean vectors," Statistica Sinicato appear.

[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] 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, to appear.​

[274] Qu, Z., and Genton, M. G. (2022), "Sparse functional boxplots for multivariate curves," Journal of Computational and Graphical Statistics, to appear.

[273] 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," Platform for Advanced Scientific Computing (PASC) Conference, to appear.

[272] Salvana, M. L., Lenzi, A., and Genton, M. G. (2022), "Spatio-temporal cross-covariance functions under the Lagrangian framework with multiple advections," Journal of the American Statistical Association, to appear.


[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.




[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. (2020), "HLIBCov: Parallel hierarchical matrix approximation of large covariance matrices and likelihoods with applications in parameter identification,"  MethodsX7:100600.​

[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 space-time covariance functions with dynamical compact supports," 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 1234rejoinder​)

[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.





[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, and Analytics (HiPC), 152-162. 

[234] Cao, J., Genton, M. G., Keyes, D. E., 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 A182, 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 Analysis131, 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,"​​ Environmetrics30:e2535.

[228] Hu, Z., Tong, T., and Genton, M. G. (2019), ​"Diagonal likelihood ratio test for 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," Stat8: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 Analysis169, 264-277.

[224] Litvinenko, A., Sun, Y., Genton, M. G., and Keyes, D. (2019), "Likelihood approximation with hierarchical matrices for large spatial datasets,"​ Computational Statistics and Data Analysis137, 115-132.​

[223] Martinez-Hernandez, I., Genton, M. G., and Gonzalez-Farias, G. (2019), "Robust depth-based estimation of the functional autoregressive mode," Computational Statistics and Data Analysis131, 66-79.​

[222] Militino, A. F., Ugarte, M. D., Perez-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 spatio-temporal model for daily wind speeds based on a multivariate skew-t distribution,"​ Journal of Time Series Analysis40, 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," Significance16(3), 10-11.

[218] Yan, Y., and Genton, M. G. (2019), "Non-Gaussian autoregressive processes with Tukey g-and-h transformations,"​ Environmetrics30:e2503.​​




[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 Systems29, 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 Analysis166, 98-110. 

[214] Castruccio, S., and Genton, M. G. (2018), "Principles for statistical inference on big spatio-temporal data from climate models," Statistics and Probability Letters136, 92-96.

[213] Castruccio, S., Ombao, H., and Genton, M. G. (2018), "A scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data,"​ Biometrics74, 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: Atmospheres123, 6443-6459.

[211] Dai, W., and Genton, M. G. (2018), "Functional boxplots for multivariate curves," Stat7:e190.

[210] Dai, W., and Genton, M. G. (2018), "Multivariate functional data visualization and outlier detection," Journal of Computational and Graphical Statistics27, 923-934.

[209] Dai, W., and Genton, M. G. (2018), "An outlyingness matrix for multivariate functional data classification," Statistica Sinica28, 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. E., 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 Statistics45, 861-878.

[204] Krupskii, P., Huser, R., and Genton, M. G. (2018), "Factor copula models for replicated spatial data,"​​​​ Journal of the American Statistical Association113, 467-479. ​

[203] Krupskii, P., Joe, H., Lee, D., and Genton, M. G. (2018), "Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Huesler-Reiss distribution," Journal of Multivariate Analysis163, 80-95.​

[202Vettori, S., Huser, R., and Genton, M. G. (2018), "A comparison of dependence function estimators in multivariate extremes,"​​​​​​ Statistics and Computing28, 525-538. 

[201] Yan, Y., and Genton, M. G. (2018), "Gaussian likelihood inference on data from trans-Gaussian random fields with Matern covariance function,"​​ Environmetrics29:e2458.​




[200] Dutta, S., and Genton, M. G. (2017), "Depth-weighted robust multivariate regression with application to sparse data,"​​​​ The Canadian Journal of Statistics45, 164-184. 

[199] Genton, M. G., and Hering, A. (2017), discussion of "Spatiotemporal models for skewed processes,"​ by A. Schmidt, K. Goncalves, P. Velozo, Environmetrics28: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 Applications4, 7.

[197] Jeong, J., Jun, M., and Genton, M. G. (2017), "Spherical process models for global spatial statistics," Statistical Science32, 501-513.

[196] Krupskii, P., and Genton, M. G. (2017), "Factor copula models for data with spatio-temporal dependence​​​,"​​ Spatial Statistics22, 180-195. 

[195] Xu, G., and Genton, M. G. (2017), "Tukey g-and-h random fields," Journal of the American Statistical Association112, 1236-1249. (Supplementary Material​​​)​




[194] Azzalini, A., Browne, R. P., Genton, M. G., and McNicholas, P. D. (2016), "On the nomenclature for, and the relative merits of, two formulations of skew distributions,"​​​ Statistics and Probability Letters110, 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. E., Genton, M. G., and Yokota, R. (2016), "Multi-level restricted maximum likelihood covariance estimation and kriging for large non-gridded spatial datasets,"​​ Spatial Statistics18, 105-124.

[191] Castruccio, S., and Genton, M. G. (2016), "Compressing an ensemble with statistical models: An algorithm for global 3D spatio-temporal temperature,"​​ Technometrics58, 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 Statistics25, 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 Biology89, 1570-1582. 

[188] Dai, W., Tong, T., and Genton, M. G. (2016), "Optimal estimation of derivatives in nonparametric regression,"​ Journal of Machine Learning Research17(164), 1-25. 

[187] Dong, K., Pang, H., Tong, T., and Genton, M. G. (2016), "Shrinkage-based diagonal Hotelling's tests for high-dimensional small sample size data,"​​ Journal of Multivariate Analysis143, 127-142.

[186] Genton, M. G., and Hall, P. (2016), "A tilting approach to ranking influence,"​ Journal of the Royal Statistical Society, Series B78, 77-97.

[185] Huser, R., Davison, A. C., and Genton, M. G. (2016), "Likelihood estimators for multivariate extremes," Extremes19, 79-103.

[184] Huser, R., and Genton, M. G. (2016), "Non-stationary dependence structures for spatial extremes,"​ Journal of Agricultural, Biological and Environmental Statistics21, 470-491. 

[183] Kim, H.-M., Maadooliat, M., Arellano-Valle, R. B., and Genton, M. G. (2016), "Skewed factor models using selection mechanisms,"​​​ Journal of Multivariate Analysis145, 162-177.

[182] Lee, M., Genton, M. G., and Jun, M. (2016), "Testing self-similarity through Lamperti transformations,"​​ Journal of Agricultural, Biological and Environmental Statistics21, 426-447. 

[181] Porcu, E., Bevilacqua, M., and Genton, M. G. (2016), "Spatio-temporal covariance and cross-covariance functions of the great circle distance on a sphere," Journal of the American Statistical Association111, 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 Series544, 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 Medicine35, 2441-2454. (Supplementary Material​​​)

[178] Xu, G., and Genton, M. G. (2016), "Tukey max-stable processes for spatial extremes,"​ Spatial Statistics18, 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 B78, 805-827.




[176] Azzalini, A., and Genton, M. G. (2015), discussion of "On families of distributions with shape parameters"​ by M. C. Jones,​ International Statistical Review83, 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,​​  TEST24, 31-34. ​

[174] Chakraborty, A., De, S., Bowman, K., Sang, H., Genton, M. G., and Mallick, B. (2015), "An adaptive spatial model for precipitation data from multiple satellites over large regions,"​ Statistics and Computing25, 389-405.

[173] Genton, M. G., Castruccio, S., Crippa, P., Dutta, S., Huser, R., Sun, Y., and Vettori, S. (2015), "Visuanimation in statistics,"​​​ Stat4, 81-96.

[172] Genton, M. G., and Kleiber, W. (2015), "Cross-covariance functions for multivariate geostatistics (with discussion),"​ Statistical Science30, 147-163. (discussion 1234​rejoinder​)

[171] Genton, M. G., Padoan, S. A., and Sang, H. (2015), "Multivariate max-stable spatial processes,"​​ Biometrika102, 215-230. (Supplementary Material​​)

[170] Goddard, S. D., Genton, M. G., Hering, A. S., and Sain, S. R. (2015), "Evaluating the impacts of climate change on diurnal wind power cycles using multiple regional climate models,"​​​ Environmetrics26, 192-201.

[169] Lee, G., Ding, Y., Genton, M. G., and Xie, L. (2015), "Power curve estimation with multivariate environmental factors for inland and offshore wind farms,"​​ Journal of the American Statistical Association110, 56-67.

[168] Lee, G., Ding, Y., Xie, L., and Genton, M. G. (2015), "A kernel plus method for quantifying wind turbine performance upgrades,"​ Wind Energy18, 1207-1219.

[167] Lee, M., Jun, M., and Genton, M. G. (2015), "Validation of CMIP5 multimodel ensembles through the smoothness of climate variables,"​ Tellus A67, 23880.

[166] Militino, A. F., Ugarte, M. D., 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., Srinivasan, R., Cramer, S., and Ombao, H. (2015), "An exploratory data analysis of electroencephalograms using the functional boxplots approach,"​ Frontiers in Neuroscience9, Article 282, 1-18.

[164] Razafindrakoto, H. N. T., Mai, P. M., Genton, M. G., Zhang, L., and Thingbaijam, K. K. S. (2015)"Quantifying variability in earthquake rupture models using multidimensional scaling: Application to the 2011 Tohoku earthquake,"​​​ Geophysical Journal International202, 17-40.​

[163] Roh, S., Jun, M., Szunyogh, I., and Genton, M. G. (2015), "Multivariate localization methods for ensemble Kalman filtering," Nonlinear Processes in Geophysics22, 723-735.

[162] Sun, Y., Bowman, K. P., Genton, M. G., and Tokay, A. (2015), "A Matern model of the spatial covariance structure of point rain rates,"​ Stochastic Environmental Research and Risk Assessment29, 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 Analysis91, 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 Sinica25, 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., Wolff, 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. K. S., Razafindrakoto, H. N. T., and Genton, M. G. (2015), "Analysing earthquake slip models with the spatial prediction comparison test,"​ Geophysical Journal International200, 185-198.




[156] Castruccio, S., and Genton, M. G. (2014), "Beyond axial symmetry: An improved class of models for global data," Stat3, 48-55. 

[155] Dao, A., and Genton, M. G. (2014), "A Monte Carlo adjusted goodness-of-fit test for parametric models describing spatial point patterns," Journal of Computational and Graphical Statistics23, 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 Analysis132, 82-93.

[153] Genton, M. G., Johnson, C., Potter, K., Stenchikov, G., and Sun, Y. (2014), "Surface boxplots," Stat3, 1-11.

[152] Kim, H.-M., Ryu, D., Mallick, B. K., and Genton, M. G. (2014), "Mixtures of skewed Kalman filters," Journal of Multivariate Analysis123, 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.