Bayesian Influence Analysis of the Skew-Normal Spatial Autoregression Models

被引：6

作者：

Ju, Yuanyuan ^{[1
,2
,3
]}

Yang, Yan ^{[1
]}

Hu, Mingxing ^{[1
]}

Dai, Lin ^{[1
]}

Wu, Liucang ^{[4
]}

机构：

[1] Kunming Univ Sci & Technol, Fac Sci, Kunming 650500, Yunnan, Peoples R China

[2] Kunming Univ Sci & Technol, State Key Lab Complex Nonferrous Met Resources Cl, Kunming 650093, Yunnan, Peoples R China

[3] Kunming Univ Sci & Technol, Fac Sci, Key Lab Ind Engn Stat Anal, Kunming 650500, Yunnan, Peoples R China

[4] Kunming Univ Sci & Technol, Ctr Appl Stat, Kunming 650500, Yunnan, Peoples R China

来源：

MATHEMATICS | 2022年 / 10卷 / 08期

基金：

中国国家自然科学基金;

关键词：

skew-normal distribution; spatial autoregression model; Bayesian local influence; Bayesian case influence; MCMC algorithm; LOCAL INFLUENCE ANALYSIS; STATISTICAL-INFERENCE; MIXED MODELS;

D O I：

10.3390/math10081306

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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 phi-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 phi-divergence. The Bayesian influence analysis formulation of spatial data is given. Simulation studies and examples verify the effectiveness of the presented methodologies.

引用

页数：19

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