Bayesian Influence Analysis of the Skew-Normal Spatial Autoregression Models

Yuanyuan Ju, Yan Yang, Mingxing Hu, Lin Dai and Liucang Wu
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Yuanyuan Ju: Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China
Yan Yang: Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China
Mingxing Hu: Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China
Lin Dai: Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China
Liucang Wu: Center for Applied Statistics, Kunming University of Science and Technology, Kunming 650500, China

Mathematics, 2022, vol. 10, issue 8, 1-19

Abstract: In spatial data analysis, outliers or influential observations have a considerable influence on statistical inference. This paper develops Bayesian influence analysis, including the local influence approach and case influence measures in skew-normal spatial autoregression models (SSARMs). The Bayesian local influence method is proposed to evaluate the impact of small perturbations in data, the distribution of sampling and prior. To measure the extent of different perturbations in SSARMs, the Bayes factor, the ϕ -divergence and the posterior mean distance are established. A Bayesian case influence measure is presented to examine the influence points in SSARMs. The potential influence points in the models are identified by Cook’s posterior mean distance and Cook’s posterior mode distance ϕ -divergence. The Bayesian influence analysis formulation of spatial data is given. Simulation studies and examples verify the effectiveness of the presented methodologies.

Keywords: skew-normal distribution; spatial autoregression model; Bayesian local influence; Bayesian case influence; MCMC algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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