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Activity Number: 138 - Modeling Applications for Backcasting, Nowcasting and Forecasting
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #304575 Presentation
Title: Model-Assisted Estimation of Mixed-Effect Model Parameters in Complex Surveys
Author(s): Eric Slud*
Companies: U.S. Census Bureau
Keywords: Model-assisted; Survey Estimation; Variance components; Complex surveys; EM Algorithm; pseudo-likelihood

This paper partially solves the problem of defining design-consistent model-assisted estimators for regression and variance-component parameters within parametric models based on complex survey data. There are several well-known papers in this area (Binder 1983, Pfeffermann et al. 1998, Korn and Graubard 2003, Rabe-Hesketh and Skrondal 2006) and some less-known but also potentially important papers (Rao et al. 2013, Feder et al. 2000), but the problem of design-consistent estimation of variance-component parameters in surveys has not previously been solved in a practically effective way.

The main contribution of the paper is to provide an EM-based solution to the design-based estimation of superpopulation variance components in important mixed-effect models based on complex surveys. The method is illustrated, and evaluated for consistency via simulation, both in two-way ANOVA and in random-intercept logistic-regression settings.

Authors who are presenting talks have a * after their name.

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