Differential Measurement Errors in Zero-Truncated Regression Models for Count Data

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Title:	Differential Measurement Errors in Zero-Truncated Regression Models for Count Data
Authors:	Huang, Yih‐Huei;Hwang, Wen‐Han;Chen, Fei‐Yin
Contributors:	淡江大學數學學系
Keywords:	Conditional score;Differential measurement errors;Surrogate condition;Zero-truncated regression model
Date:	2011-12
Issue Date:	2013-04-25 15:54:53 (UTC+8)
Publisher:	Chichester: Wiley-Blackwell Publishing Ltd.
Abstract:	Measurement errors in covariates may result in biased estimates in regression analysis. Most methods to correct this bias assume nondifferential measurement errors-i.e., that measurement errors are independent of the response variable. However, in regression models for zero-truncated count data, the number of error-prone covariate measurements for a given observational unit can equal its response count, implying a situation of differential measurement errors. To address this challenge, we develop a modified conditional score approach to achieve consistent estimation. The proposed method represents a novel technique, with efficiency gains achieved by augmenting random errors, and performs well in a simulation study. The method is demonstrated in an ecology application.
Relation:	Biometrics 67(4), pp.1471-1480
DOI:	10.1111/j.1541-0420.2011.01594.x
Appears in Collections:	[Graduate Institute & Department of Mathematics] Journal Article

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