A Skew-Gaussian‎ ‎Spatio-Temporal Process with Non-Stationary Correlation Structure

Authors

1 Department of Statistics, Faculty of Mathematical Sciences, Shahid Beheshti University, Iran.

2 Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Iran.

10.29252/jirss.18.2.63

Abstract

This paper develops a new class of spatio-temporal process models that can simultaneously capture skewness and non-stationarity. The proposed approach which is based on using the closed skew-normal distribution in the low-rank representation of stochastic processes, has several favorable properties. In particular, it greatly reduces the dimension of the spatio-temporal latent variables and induces flexible correlation structures. Bayesian analysis of the model is implemented through a Gibbs MCMC algorithm which incorporates a version of the Kalman filtering algorithm. All fully conditional posterior distributions have closed forms which show another advantageous property of the proposed model. We demonstrate the efficiency of our model through an extensive simulation study and an application to a real data set comprised of precipitation measurements.

Keywords

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Volume 18, Issue 2
December 2019
Pages 63-85
  • Receive Date: 23 July 2022
  • Revise Date: 13 May 2024
  • Accept Date: 23 July 2022