BIAS ROBUST ESTIMATION IN FINITE POPULATIONS USING NONPARAMETRIC CALIBRATION

• 成果类型: Article
• 署名作者: CHAMBERS, RL; DORFMAN, AH; WEHRLY, TE
• 署名单位: United States Department of Labor; Texas A&M University System; Texas A&M University College Station; Australian National University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.1993.10594319
• 发表日期: 1993
• 页码: 268-277
• 关键词: regression estimation; CHOICE

摘要

A standard problem in sample survey inference is that of predicting the finite population total H of a function h(y) of a random variable Y. The model-based approach to this problem first defines a working model xi for Y and then predicts H by estimating its expectation under xi, conditional on the sample values of Y. This approach leads to biased predictions if xi is incorrect. We explore an automatic solution to this misspecification bias that uses nonparametric regression to define a robust (but inefficient) predictor of H, and then calibrates this predictor for its bias under xi. An application to prediction of the finite population distribution function of a population of Australian beef farms is presented.