Differential measurement errors in zero-truncated regression models for count data |
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Authors: | Huang Yih-Huei Hwang Wen-Han Chen Fei-Yin |
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Institution: | Department of Mathematics, Tamkang University, New Taipei City, Taiwan Institute of Statistics, National Chung Hsing University, Taichung, Taiwan. |
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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. |
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Keywords: | Conditional score Differential measurement errors Surrogate condition Zero‐truncated regression model |
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