Bayesian approach to errors-in-variables in count data regression models with departures from normality and overdispersion
In most practical applications, the quality of count data is often compromised due to errors-in-variables (EIVs). In this paper, we apply Bayesian approach to reduce bias in estimating the parameters of count data regression models that have mismeasured independent variables. Furthermore, the exposu...
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| Format: | Article |
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Taylor & Francis
2018
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