MODELING LOG-LINEAR CONDITIONAL PROBABILITIES FOR ESTIMATION IN SURVEYS

• 成果类型: Article
• 署名作者: Thibaudeau, Yves; Slud, Eric; Gottschalck, Alfred
• 署名单位: University System of Maryland; University of Maryland College Park
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/16-AOAS1012
• 发表日期: 2017
• 页码: 680-697
• 关键词: multiway contingency-tables; small-area estimation

摘要

The Survey of Income and Program Participation (SIPP) is a survey with a longitudinal structure and complex nonignorable design, for which correct estimation requires using the weights. The longitudinal setting also suggests conditional-independence relations between survey variables and early-versus late-wave employment classifications. We state original assumptions justifying an extension of the partially model-based approach of Pfeffermann, Skinner and Humphreys [J. Roy. Statist. Soc. Ser. A 161 (1998) 13-32], accounting for the design of SIPP and similar longitudinal surveys. Our assumptions support the use of log-linear models of longitudinal survey data. We highlight the potential they offer for simultaneous bias-control and reduction of sampling error relative to direct methods when applied to small subdomains and cells. Our assumptions allow us to innovate by showing how to rigorously use only a longitudinal survey to estimate a complex log-linear longitudinal association structure and embed it in cross-sectional totals to construct estimators that can be more efficient than direct estimators for small cells.