Brazilian Journal of Probability and Statistics

The log-generalized modified Weibull regression model

Edwin M. M. Ortega, Gauss M. Cordeiro, and Jalmar M. F. Carrasco

Full-text: Open access

Abstract

For the first time, we introduce the log-generalized modified Weibull regression model based on the modified Weibull distribution [Carrasco, Ortega and Cordeiro Comput. Statist. Data Anal. 53 (2008) 450–462]. This distribution can accommodate increasing, decreasing, bathtub and unimodal shaped hazard functions. A second advantage is that it includes classical distributions reported in lifetime literature as special cases. We also show that the new regression model can be applied to censored data since it represents a parametric family of models that includes as submodels several widely known regression models and therefore can be used more effectively in the analysis of survival data. We obtain maximum likelihood estimates for the model parameters by considering censored data and evaluate local influence on the estimates of the parameters by taking different perturbation schemes. Some global-influence measurements are also investigated. In addition, we define martingale and deviance residuals to detect outliers and evaluate the model assumptions. We demonstrate that our extended regression model is very useful to the analysis of real data and may give more realistic fits than other special regression models.

Article information

Source
Braz. J. Probab. Stat., Volume 25, Number 1 (2011), 64-89.

Dates
First available in Project Euclid: 3 December 2010

Permanent link to this document
https://projecteuclid.org/euclid.bjps/1291387774

Digital Object Identifier
doi:10.1214/09-BJPS110

Mathematical Reviews number (MathSciNet)
MR2746493

Zentralblatt MATH identifier
1300.62019

Keywords
Censored data generalized modified Weibull distribution log-Weibull regression residual analysis sensitivity analysis survival function

Citation

Ortega, Edwin M. M.; Cordeiro, Gauss M.; Carrasco, Jalmar M. F. The log-generalized modified Weibull regression model. Braz. J. Probab. Stat. 25 (2011), no. 1, 64--89. doi:10.1214/09-BJPS110. https://projecteuclid.org/euclid.bjps/1291387774


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