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Sastry's 10-year study of New Orleans Katrina evacuees shows demographic differences between returning and nonreturning

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Deirdre Bloome wins ASA award for work on racial inequality and intergenerational transmission

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David Lam is new director of Institute for Social Research

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Next Brown Bag

Monday, Oct 12
Joe Grengs, Policy & Planning for Social Equity in Transportation

Michael R. Elliott photo

Model Averaging Methods for Weight Trimming in Generalized Linear Regression Models

Publication Abstract

Elliott, Michael R. 2009. "Model Averaging Methods for Weight Trimming in Generalized Linear Regression Models." Journal of Official Statistics, 25(1): 1-20.

In sample surveys where units have unequal probabilities of inclusion, associations between the inclusion probability and the statistic of interest can induce bias in unweighted estimates. This is true even in regression models, where the estimates of the population slope may be biased if the underlying mean model is misspecified or the sampling is nonignorable. Weights equal to the inverse of the probability of inclusion are often used to counteract this bias. Highly disproportional sample designs have highly variable weights; weight trimming reduces large weights to a maximum value, reducing variability but introducing bias. Most standard approaches are ad hoc in that they do not use the data to optimize bias-variance trade-offs. This article uses Bayesian model averaging to create "data driven" weight trimming estimators. We extend previous results for linear regression models (Elliott 2008) to generalized linear regression models, developing robust models that approximate fully-weighted estimators when bias correction is of greatest importance, and approximate unweighted estimators when variance reduction is critical.

PMCID: PMC3530169. (Pub Med Central)

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