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Linear Regression Diagnostics in Cluster Samples

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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)

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.

Suggested Citation

  • Li Jianzhu & Valliant Richard, 2015. "Linear Regression Diagnostics in Cluster Samples," Journal of Official Statistics, Sciendo, vol. 31(1), pages 61-75, March.
    • Handle: RePEc:vrs:offsta:v:31:y:2015:i:1:p:61-75:n:3
    DOI: 10.1515/jos-2015-0003
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    References listed on IDEAS

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    1. Zaslavsky A. M. & Schenker N. & Belin T. R., 2001. "Downweighting Influential Clusters in Surveys: Application to the 1990 Post Enumeration Survey," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 858-869, September.
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