Estimating from cross-sectional categorical data subject to misclassification and double sampling: moment-based, maximum likelihood and quasi-likelihood approaches
Tzavidis, Nikos and Lin, Yan-Xia (2004) Estimating from cross-sectional categorical data subject to misclassification and double sampling: moment-based, maximum likelihood and quasi-likelihood approaches. Journal of Applied Mathematics and Decision Sciences, 2006, (42030), 1-13. (doi:10.1155/JAMDS/2006/42030).
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We discuss alternative approaches for estimating from cross-sectional categorical data in the presence of misclassification. Two parameterisations of the misclassification model
are reviewed. The first employs misclassification probabilities and leads tomoment-based inference. The second employs calibration probabilities and leads to maximumlikelihood
inference. We show that maximum likelihood estimation can be alternatively performed by employing misclassification probabilities and a missing data specification.
As an alternative to maximum likelihood estimation we propose a quasi-likelihood parameterisation of the misclassification model. In this context an explicit definition of the likelihood function is avoided and a different way of resolving a missing data problem is provided. Variance estimation for the alternative point estimators is considered. The different approaches are illustrated using real data from the UK Labour Force Survey and simulated data.
|Subjects:||H Social Sciences > HA Statistics|
|Divisions:||University Structure - Pre August 2011 > School of Social Sciences > Social Statistics
|Date Deposited:||19 May 2006|
|Last Modified:||06 Aug 2015 02:31|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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