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June 2017 Modeling log-linear conditional probabilities for estimation in surveys
Yves Thibaudeau, Eric Slud, Alfred Gottschalck
Ann. Appl. Stat. 11(2): 680-697 (June 2017). DOI: 10.1214/16-AOAS1012


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.


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Yves Thibaudeau. Eric Slud. Alfred Gottschalck. "Modeling log-linear conditional probabilities for estimation in surveys." Ann. Appl. Stat. 11 (2) 680 - 697, June 2017.


Received: 1 May 2010; Revised: 1 December 2016; Published: June 2017
First available in Project Euclid: 20 July 2017

zbMATH: 06775888
MathSciNet: MR3693542
Digital Object Identifier: 10.1214/16-AOAS1012

Keywords: conditional probability , Horvitz–Thompson estimator , Log-linear model , model calibration

Rights: Copyright © 2017 Institute of Mathematical Statistics

Vol.11 • No. 2 • June 2017
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