Robust model-based and model-assisted predictors of the finite population total

• 成果类型: Article
• 署名作者: Li, Yan; Lahiri, P.
• 署名单位: University System of Maryland; University of Maryland College Park; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI)
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214507000000158
• 发表日期: 2007
• 页码: 664-673
• 关键词: EMPIRICAL BAYES ESTIMATION; box-cox transformation; functional form; demand; parameters; variance; VALUES

摘要

The prediction approach to finite population inference has received considerable attention in recent years. Under this approach, the finite population is assumed to be a realization from a superpopulation described by a known probability model, usually a linear model. The prediction approach is often criticized for its lack of robustness against model misspecification. In this article we revisit this important issue and introduce a new robust prediction approach in which the superpopulation model is chosen adaptively from the well-known BoxCox class of probability distributions. The richness of the Box-Cox class ensures robustness in our model-based prediction approach. We explain how our robust model-based predictor can be adjusted to handle zero observations for the study variable and to achieve the design-unbiasedness and benchmarking properties. We demonstrate the robustness of our proposed predictors using a Monte Carlo simulation study and a real life example.