Bayesian spatial regression models with closed skew normal correlated errors and missing observations

DOI10.1007/s00362-010-0329-2zbMath1241.62023OpenAlexW2032653180MaRDI QIDQ434397

Mohsen Mohammadzadeh, Omid Karimi

Publication date: 10 July 2012

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-010-0329-2

zbMATH Keywords

MCMCBayesian prediction

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Linear regression; mixed models (62J05) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

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Some properties of the unified skew-normal distribution ⋮ Kalman filter variants in the closed skew normal setting ⋮ Modeling additive genetic effects in animal models by closed skew normal distribution ⋮ Approximate likelihood inference in spatial generalized linear mixed models with closed skew normal latent variables ⋮ Bayesian spatial prediction for discrete closed skew Gaussian random field ⋮ A skewed unscented Kalman filter ⋮ Kernel regression estimation for incomplete data with applications ⋮ Kernel classification with missing data and the choice of smoothing parameters ⋮ Quantile regression and its empirical likelihood with missing response at random ⋮ Approximate pairwise likelihood inference in SGLM models with skew normal latent variables ⋮ Bayesian prediction of spatial data with non-ignorable missingness ⋮ Sure independence screening in the presence of missing data ⋮ Performance of extrapolation based on Pitman's measure of closeness in spatial regression models with extended skew t innovations

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