Case-deletion diagnostics for mixed models
Technometrics
Log-Burr XII regression models with censored data
Computational Statistics & Data Analysis
Log-modified Weibull regression models with censored data: Sensitivity and residual analysis
Computational Statistics & Data Analysis
Assessment of diagnostic procedures in symmetrical nonlinear regression models
Computational Statistics & Data Analysis
Influence analyses of skew-normal/independent linear mixed models
Computational Statistics & Data Analysis
Short communication: On quantile quantile plots for generalized linear models
Computational Statistics & Data Analysis
A finite mixture of bivariate Poisson regression models with an application to insurance ratemaking
Computational Statistics & Data Analysis
A hyper-Poisson regression model for overdispersed and underdispersed count data
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
The zero-inflated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-inflated Poisson model. A frequentist analysis, a jackknife estimator and a non-parametric bootstrap for parameter estimation of zero-inflated negative binomial regression models are considered. In addition, an EM-type algorithm is developed for performing maximum likelihood estimation. Then, the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and some ways to perform global influence analysis are derived. In order to study departures from the error assumption as well as the presence of outliers, residual analysis based on the standardized Pearson residuals is discussed. The relevance of the approach is illustrated with a real data set, where it is shown that zero-inflated negative binomial regression models seems to fit the data better than the Poisson counterpart.