title: Differential measurement errors in zero-truncated regression models for count data.
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.
authors.affiliation: Department of Mathematics, Tamkang University, New Taipei City, Taiwan Institute of Statistics, National Chung Hsing University, Taichung, Taiwan.
authors.forename: Yih-Huei
authors.initials: YH
authors.lastname: Huang
authors.forename: Wen-Han
authors.initials: WH
authors.lastname: Hwang
authors.forename: Fei-Yin
authors.initials: FY
authors.lastname: Chen
mesh_terms: D000818:Animals; D000886:Anthropometry / Q000379:methods*; D016477:Artifacts; D001699:Biometry / Q000379:methods*; D001835:Body Weight / Q000502:physiology*; D003198:Computer Simulation; D051379:Mice; D015233:Models, Statistical; D012044:Regression Analysis; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity