Using double sampling to correct gross flows for misclassification error: moment-based inference vs. likelihood-based inference
Tzavidis, Nikolaos (2004) Using double sampling to correct gross flows for misclassification error: moment-based inference vs. likelihood-based inference. In, JSM 2004: Statistics as a Unified Discipline, Toronto, Canada, 08 - 12 Aug 2004. Toronto: Canada,
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Longitudinal surveys provide a key source of information for analyzing dynamic phenomena. Typical examples of longitudinal data are gross flows between a finite number of states. Sample surveys are, however, affected by nonsampling errors. We investigate the use of double sampling for correcting discrete longitudinal data for misclassification error. In a double sampling context, we assume that along with the main measurement device, which is affected by misclassification error, we can use a secondary measurement device, which is free of error but more expensive to apply. Inference is based on combining information from both measurement devices. Traditional moment-based inference is reviewed and contrasted, under alternative double sampling schemes, with a proposed likelihood-based method that works by simultaneously modeling the true transition process and the measurement error process within the context of a missing data problem. Variance estimation, under both approaches, is discussed. Monte Carlo simulation experiments indicate that the proposed likelihood-based method offers significant gains in efficiency over the traditional moment-based method.
|Item Type:||Conference or Workshop Item (Paper)|
|Keywords:||measurement error, missing data, panel surveys, EM algorithm, re-interview surveys, missing information principle|
|Subjects:||H Social Sciences > HA Statistics|
|Divisions:||University Structure - Pre August 2011 > School of Social Sciences > Social Statistics
|Date Deposited:||16 May 2006|
|Last Modified:||27 Mar 2014 18:21|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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