Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys

J. Chen

Biometrika, 2002, vol. 89, issue 1, 230-237

Abstract: Design weights in surveys are often adjusted to accommodate auxiliary information and to meet pre-specified range restrictions, typically via some ad hoc algorithmic adjustment to a generalised regression estimator. In this paper, we present a simple solution to this problem using empirical likelihood methods or generalised regression. We first develop algorithms for computing empirical likelihood estimators and model-calibrated empirical likelihood estimators. The first algorithm solves the computational problem of the empirical likelihood method in general, both in survey and non-survey settings, and theoretically guarantees its convergence. The second exploits properties of the model-calibration method and is particularly simple. The algorithms are adapted for handling benchmark constraints and pre-specified range restrictions on the weight adjustments. Copyright Biometrika Trust 2002, Oxford University Press.

Date: 2002
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