A flexible special case of the CSN for spatial modeling and prediction

Spatial Statistics - Tập 47 - Trang 100556 - 2022
José Ulises Márquez-Urbina1,2, Graciela González-Farías3
1Centro de Investigación en Matemáticas Unidad Monterrey, Av. Alianza Centro No. 502, Parque de Investigación e Innovación Tecnológica (PIIT), Apodaca, 66628, N.L., Mexico
2Consejo Nacional de Ciencia y Tecnología, Av. Insurgentes Sur 1582, Col. Crédito Constructor, Benito Juárez, 03940, CDMX, Mexico
3Centro de Investigación en Matemáticas, Jalisco s/n, Valenciana, Guanajuato, 36023, GTO, México

Tài liệu tham khảo

Allard, 2007, A new spatial skew-normal random field model, Comm. Statist. Theory Methods, 36, 1821, 10.1080/03610920601126290 Arnold, 2002, Skewed multivariate models related to hidden truncation and/or selective reporting, Test, 11, 7, 10.1007/BF02595728 Billingsley, 1995 Bjornstad, 1990, Predictive likelihood: A review, Statist. Sci., 5, 242 Copas, 1997, Inference for non-random samples, J. R. Stat. Soc. Ser. B Stat. Methodol., 59, 55, 10.1111/1467-9868.00055 Díaz-García, 2008, Singular extended skew-elliptical distributions, J. Korean Stat. Soc., 37, 385, 10.1016/j.jkss.2008.04.004 Domínguez-Molina, 2003 Domínguez-Molina, 2007, A matrix variate closed skew-normal distribution with applications to stochastic frontier analysis, Comm. Statist. Theory Methods, 36, 1691, 10.1080/03610920601126126 González-Farías, 2004, Additive properties of skew normal random vectors, J. Statist. Plann. Inference, 126, 521, 10.1016/j.jspi.2003.09.008 González-Farías, 2004, The closed skew-normal distribution, 25 Gräler, 2016, Spatio-temporal interpolation using gstat, R J., 8, 204, 10.32614/RJ-2016-014 Gupta, 2004, A multivariate skew normal distribution, J. Multivariate Anal., 89, 181, 10.1016/S0047-259X(03)00131-3 Karimi, 2011, Bayesian spatial prediction for discrete closed skew Gaussian random field, Math. Geosci., 43, 565, 10.1007/s11004-011-9341-x Karimi, 2012, Bayesian spatial regression models with closed skew normal correlated errors and missing observations, Statist. Papers, 53, 205, 10.1007/s00362-010-0329-2 Kou, 2007, Spatial outlier detection: A graph-based approach, Vol. 1, 281 Kreutz, 2013, Profile likelihood in systems biology, FEBS J., 280, 2564, 10.1111/febs.12276 Mahmoudian, 2018, On the existence of some skew-Gaussian random field models, Statist. Probab. Lett., 137, 331, 10.1016/j.spl.2018.02.052 Mathiasen, 1979, Prediction functions, Scand. J. Stat., 6, 1 Minozzo, 2012, On the existence of some skew-normal stationary processes, Chilean J. Stat., 3, 157 Nash, 2014, On best practice optimization methods in R, J. Stat. Softw., 60, 1, 10.18637/jss.v060.i02 Nash, 2011, Unifying optimization algorithms to aid software system users: optimx for R, J. Stat. Softw., 43, 1, 10.18637/jss.v043.i09 Pavlyuk, 2015 Pebesma, 2004, Multivariable geostatistics in S: the gstat package, Comput. Geosci., 30, 683, 10.1016/j.cageo.2004.03.012 Ramaswamy, S., Rastogi, R., Shim, K., 2000. Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. pp. 427–438. Rimstad, 2014, Skew-Gaussian random fields, Spatial Stat., 10, 43, 10.1016/j.spasta.2014.08.001 Tagle, 2019, A non-Gaussian spatio-temporal model for daily wind speeds based on a multi-variate skew-t distribution, J. Time Series Anal., 40, 312, 10.1111/jtsa.12437 Yamanishi, 2004, On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms, Data Min. Knowl. Discov., 8, 275, 10.1023/B:DAMI.0000023676.72185.7c