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

• 成果类型: Article
• 署名作者: Chen, J; Sitter, RR; Wu, C
• 署名单位: University of Waterloo; Simon Fraser University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/89.1.230
• 发表日期: 2002
• 页码: 230237
• 关键词: Auxiliary Information

摘要

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.