JSM 2004 - Toronto

Abstract #300752

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Activity Number: 215
Type: Topic Contributed
Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Survey Research Methods
Abstract - #300752
Title: Hierarchical Bayes Inference to Small-area Census Undercoverage Estimation
Author(s): Yong You*+ and Peter Dick
Companies: Statistics Canada and Statistics Canada
Address: Household Survey Methods Division, Ottawa, ON, K1A 0T6, Canada
Keywords: benchmarking ; census undercoverage ; hierarchical Bayes ; sampling variance ; small area
Abstract:

We will review and outline some hierarchical Bayes (HB) small-area models, the problem of sampling variances, and the calibration procedures for Canadian small-area census undercoverage estimation. The small-area models include the well-known Fay-Herriot model and the unmatched area level models. In general, sampling variances are assumed to be known in these models. Usually smoothed estimators of the sampling variances are used and then treated as known. We also consider the case of estimated sampling variances and a full HB model is constructed for the direct survey estimators and the direct estimators of sampling variances. Finally the HB estimates are calibrated to agree with national or provincial totals using the benchmarking HB method of You, Rao, and Dick (2002, 2004). Posterior mean squared error (PMSE) will be used as a measure of uncertainty for the benchmarked HB estimators. Examples in census undercoverage estimation will be provided.

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