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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Description/Abstract

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)
Related URLs:
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
ePrint ID: 34710
Date Deposited: 16 May 2006
Last Modified: 27 Mar 2014 18:21
URI: http://eprints.soton.ac.uk/id/eprint/34710

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