Estimation in zero-inflated binomial regression with missing covariates

Estimation in zero-inflated binomial regression with missing covariates

Model selection for zero-inflated regression with missing covariates

Count data are widely existed in the fields of medical trials, public health, surveys and environmental studies. In analyzing count data, it is important to find outwhether the zeroinflation exists or not and how to select the most suitable model. However, the classic AIC criterion formodel selection is invalid when the observations aremissing. In this paper, we develop a new model selection cr...

Zero inflated Poisson and negative binomial regression models: application in education

Background: The number of failed courses and semesters in students are indicatorsof their performance. These amounts have zero inflated (ZI) distributions. Using ZI Poisson and negative binomial distributions we can model these count data to find the associated factors and estimate the parameters. This study aims at to investigate the important factors related to the educational performance of ...

Parameter Estimation on Zero-Inflated Negative Binomial Regression with Right Truncated Data

A Poisson model typically is assumed for count data, but when there are so many zeroes in the response variable, because of overdispersion, a negative binomial regression is suggested as a count regression instead of Poisson regression. In this paper, a zero-inflated negative binomial regression model with right truncation count data was developed. In this model, we considered a response variab...

Semiparametric Maximum Likelihood Estimation in Parametric Regression with Missing Covariates