Small-domain estimation from statistics with measurement error
by Professor Alan Zaslavsky
Abstract: 5:45pm â€“ Light refreshments in the Staff Tea Room, Richard Berry Building, University of Melbourne.
Commonly the large administrative, census, or survey datasets required to make estimates for small domains (areas, institutions, etc.) measure some important variables with nonsampling error. Supplementary information from a smaller survey may make it possible to estimate models for this measurement error and correct or calibrate small-domain estimates. This general structure is illustrated with three examples, each requiring a different model structure appropriate to the form of the available data and the error process: (1) imputation of corrected adjuvant therapy indicators in a cancer registry subect to underreporting of treatment; (2) estimation of school-level prevalence of serious emotional distress using a short screening scale; (3) combining information from a census, a post-enumeration survey, and followup evaluation studies to improve estimates of population. In each application a Bayesian hierarchical model is used to synthesize information from multiple sources.
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