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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 At JSM 2004: Statistics as a Unified Discipline. 08 - 12 Aug 2004.

Record type: Conference or Workshop Item (Paper)


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.

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Published date: 2004
Venue - Dates: JSM 2004: Statistics as a Unified Discipline, 2004-08-08 - 2004-08-12
Keywords: measurement error, missing data, panel surveys, EM algorithm, re-interview surveys, missing information principle


Local EPrints ID: 34710
PURE UUID: cc36a3fc-4811-450c-ac82-282a7d526bdf

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Date deposited: 16 May 2006
Last modified: 17 Jul 2017 15:49

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