Downweighting Influential Clusters in Surveys
- 1 September 2001
- journal article
- Published by Taylor & Francis in Journal of the American Statistical Association
- Vol. 96 (455) , 858-869
- https://doi.org/10.1198/016214501753208889
Abstract
Certain clusters may be extremely influential on survey estimates and consequently contribute disproportionately to their variance. We propose a general approach to estimation that downweights highly influential clusters, with the amount of downweighting based on M-estimation applied to the empirical influence of the clusters. The method is motivated by a problem in census coverage estimation, and we illustrate it by using data from the 1990 Post Enumeration Survey (PES). In this context, an objective, prespecified methodology for handling influential observations is essential to avoid having to justify judgmental post hoc adjustment of weights. In 1990, both extreme weights and large errors in the census led to extreme influence. We estimated influence by Taylor linearization of the survey estimator, and we applied M-estimators based on the t distribution and the Huber ψ-function. As predicted by theory, the robust procedures greatly reduced the estimated variance of estimated coverage rates, more so tha...Keywords
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