Linear Regression Influence Diagnostics for Unclustered Survey Data
Jianzhu Li, Richard Valliant
Diagnostics for linear regression models have largely been developed to handle nonsurvey data. The models and the sampling plans used for finite populations often entail stratification, clustering, and survey weights. In this article we adapt some influence diagnostics that have been formulated for ordinary or weighted least squares for use with unclustered survey data. The statistics considered here include DFBETAS, DFFITS, and Cooks D. The differences in the performance of ordinary least squares and survey-weighted diagnostics are compared in an empirical study where the values of weights, response variables, and covariates vary substantially.
Complex sample, Cooks D, DFBETAS, DFFITS, influence, outlier, residual analysis