FLEXIBLE BAYESIAN SPATIAL MODELING FOR UNKNOWN MISSING DATA MECHANISM IN SURVEY ANALYSIS: AN APPLICATION TO THE CHINESE GENERAL SOCIETY SURVEY

• 成果类型: Article
• 署名作者: Hu, Guanyu; Chen, Ming-Hui; Ma, Zhihua
• 署名单位: University of Texas System; University of Texas Health Science Center Houston; University of Connecticut; Shenzhen University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS2004
• 发表日期: 2025
• 页码: 1714-1733
• 关键词: linear-models; inference

摘要

Social survey data are often collected from different survey centers in different regions. In some circumstances the response variables are completely observed while the covariates have missing values. In addition, the missing data patterns will vary in different geographical locations. In this article an ordinal response regression model with a log linear model for the cutpointsis extended to fit the spatial ordinal response data with missing covariates within a Bayesian framework. A signed-spike-and-slab prior is developed to learn the heterogeneity of the missing data mechanisms among different spatial locations. The properties of the proposed models are examined, and a Markov chain Monte Carlo sampling algorithm is used to sample from the posterior distribution. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. We further apply the proposed methodology to analyze a real dataset from a Chinese General Society Survey.