Influence Diagnostic in Log-Exponential-Inverse- Exponential {Weibull} Regression Failure Model

Abstract

An exponential-inverse-exponential {Weibull} regression failure model is introduced. Some of its properties like density function, survival function, and hazard function are derived. Maximum likelihood estimates of the parameters of the new model from censored data are obtained. To assess the local influence diagnostic(s) on the parameter estimates, the appropriate matrices are derived. Also, global influence and local influence are used to detect influential observations. Martingale and Deviance residuals are obtained and used to detect outliers and evaluate the model assumptions. A real data is analyzed under Log-Exponential-Inverse-Exponential {Weibull} regression model to show the usefulness of the model. A simulation study is performed to investigate the behavior of the estimates for different sample sizes and censoring percentages.