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Activity Number:
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260
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #300357 |
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Title:
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A Model-based Approach to Weight-trimming
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Author(s):
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Michael R. Elliott*+
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Companies:
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University of Pennsylvania School of Medicine
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Address:
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Center for Clinical Epidemiology and Biostatistics, Philadelphia, PA, 19104,
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Keywords:
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sample survey inference ; sampling weights ; regression estimators ; random effects
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Abstract:
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In unequal-probability-of-selection sample, correlations between the probability of selection and the sampled data can induce bias. Weights equal to the inverse of the probability of selection are often used to counteract this bias. Highly disproportional sample designs have large weights, which can introduce unnecessary variability in statistics such as the population mean estimate. Weight-trimming reduces large weights to a fixed cutpoint value and adjusts weights below this value to maintain the untrimmed weight sum. This reduces variability at the cost of introducing some bias. Standard approaches are not "data-driven": they do not use the data to make the appropriate bias-variance trade-off, or else do so in a highly inefficient fashion. This presentation develops Bayesian methods for weight-trimming to supplement standard, ad hoc design-based methods in disproportional probability-of-inclusion designs where variances due to sample weights exceeds bias correction. These methods are used to estimate linear and generalized linear regression model population parameters in the context of stratified and poststratified known-probability sample designs.
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