A bivariate regression model for matched paired survival data: local influence and residual analysis

2010 ◽  
Vol 19 (4) ◽  
pp. 477-495 ◽  
Author(s):  
Gladys D. C. Barriga ◽  
Francisco Louzada-Neto ◽  
Edwin M. M. Ortega ◽  
Vicente G. Cancho
Keyword(s):  
Regression Model ◽  
Survival Data ◽  
Residual Analysis ◽  
Local Influence ◽  
Bivariate Regression

The Bivariate Regression Model

2018 ◽  
pp. 15-28
Author(s):  
William D. Berry ◽  
Mitchell S. Sanders
Keyword(s):  
Regression Model ◽  
Bivariate Regression

scholarly journals New Flexible Regression Models Generated by Gamma Random Variables with Censored Data

2016 ◽  
Vol 5 (3) ◽  
pp. 9 ◽  
Author(s):  
Elizabeth M. Hashimoto ◽  
Gauss M. Cordeiro ◽  
Edwin M.M. Ortega ◽  
G.G. Hamedani
Keyword(s):  
Regression Model ◽  
Censored Data ◽  
Survival Data ◽  
Regression Models ◽  
Empirical Distribution ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Standard Normal Distribution ◽  
Model Parameters ◽  
Linear Regression Models

We propose and study a new log-gamma Weibull regression model. We obtain explicit expressions for the raw and incomplete moments, quantile and generating functions and mean deviations of the log-gamma Weibull distribution. We demonstrate that the new regression model can be applied to censored data since it represents a parametric family of models which includes as sub-models several widely-known regression models and therefore can be used more effectively in the analysis of survival data. We obtain the maximum likelihood estimates of 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. Further, for different parameter settings, sample sizes and censoring percentages, various simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. 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. 

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