Correcting for misclassification error in gross flows using double sampling: moment-based inference vs. likelihood-based inference


Tzavidis, Nikos (2004) Correcting for misclassification error in gross flows using double sampling: moment-based inference vs. likelihood-based inference. Southampton, UK, University of Southampton, 34pp. (S3RI Methodology Working Papers, (M04/11) ).

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Description/Abstract

Gross flows are discrete longitudinal data that are defined as transition counts, between a finite number of states, from one point in time to another. We discuss the analysis of gross flows in the presence of misclassification error via double sampling methods. Traditionally, adjusted for misclassification error estimates are obtained using a moment-based estimator. We propose a likelihood-based approach that works by simultaneously modeling the true transition process and the misclassification error process within the context of a missing data problem. Monte-Carlo simulation results indicate that the maximumlikelihood estimator is more efficient than the moment-based estimator.

Item Type: Monograph (Working Paper)
Subjects: H Social Sciences > HA Statistics
Divisions: University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
ePrint ID: 9191
Date Deposited: 20 Sep 2004
Last Modified: 27 Mar 2014 18:01
Publisher: University of Southampton
URI: http://eprints.soton.ac.uk/id/eprint/9191

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