Analysis of mixed outcomes: misclassified binary responses and measurement error in covariates

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/10479

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DC Field	Value	Language
dc.contributor.author	Roy, Surupa
dc.contributor.author	Banerjee, Tathagata
dc.date.accessioned	2011-05-04T05:16:41Z
dc.date.available	2011-05-04T05:16:41Z
dc.date.copyright	2009-04-15
dc.date.issued	2009-04-15T05:16:41Z
dc.identifier.uri	http://hdl.handle.net/11718/10479
dc.description	Journal of Statistical Computation and Simulation, 79, 12 (2009).	en
dc.description.abstract	This paper considers regression models for mixed binary and continuous outcomes, when the true predictor is measured with error and the binary responses are subject to classification errors. The focus of the paper is to study the effects of these errors on the estimates of the model parameters and also to propose a model that incorporates both these errors. The proposed model results in a substantial improvement in the estimates as shown by extensive simulation studies.
dc.language.iso	en	en
dc.title	Analysis of mixed outcomes: misclassified binary responses and measurement error in covariates	en
dc.type	Article	en
Appears in Collections:	Journal Articles

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