Linear Regression Diagnostics in Cluster Samples

Li Jianzhu () and Valliant Richard ()
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Li Jianzhu: Westat, 1600 Research Boulevard, Rockville MD 20850, USA
Valliant Richard: Universities of Michigan and Maryland, 1218 Lefrak Hall, College Park MD 20742, USA

Journal of Official Statistics, 2015, vol. 31, issue 1, 61-75

Abstract: An extensive set of diagnostics for linear regression models has been developed to handle nonsurvey data. The models and the sampling plans used for finite populations often entail stratification, clustering, and survey weights, which renders many of the standard diagnostics inappropriate. In this article we adapt some influence diagnostics that have been formulated for ordinary or weighted least squares for use with stratified, clustered survey data. The statistics considered here include DFBETAS, DFFITS, and Cook's D. The differences in the performance of ordinary least squares and survey-weighted diagnostics are compared using complex survey data where the values of weights, response variables, and covariates vary substantially.

Keywords: Cook's D; DFBETAS; DFFITS; influence; model fitting; outlier; residuals (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:31:y:2015:i:1:p:61-75:n:3

DOI: 10.1515/jos-2015-0003

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