On a class of non‐linear transformation cure rate models

2020 ◽  
Vol 62 (5) ◽  
pp. 1208-1222 ◽  
Author(s):  
Narayanaswamy Balakrishnan ◽  
Fotios S. Milienos
Keyword(s):  
Linear Transformation ◽  
Cure Rate ◽  
Cure Rate Models ◽  
Non Linear

Relaxed Poisson cure rate models

2015 ◽  
Vol 58 (2) ◽  
pp. 397-415 ◽  
Author(s):  
Josemar Rodrigues ◽  
Gauss M. Cordeiro ◽  
Vicente G. Cancho ◽  
N. Balakrishnan
Keyword(s):  
Cure Rate ◽  
Cure Rate Models

Iterations of a non-linear transformation for enhancement of digital images

1975 ◽  
Vol 7 (1-2) ◽  
pp. 53-58 ◽  
Author(s):  
Henry P. Kramer ◽  
Judith B. Bruckner
Keyword(s):  
Linear Transformation ◽  
Digital Images ◽  
Non Linear

Bayesian Approaches to Cure Rate Models

2005 ◽  
Author(s):  
Joseph G. Ibrahim ◽  
Ming-Hui Chen ◽  
Debajyoti Sinha
Keyword(s):  
Cure Rate ◽  
Bayesian Approaches ◽  
Cure Rate Models

scholarly journals Gradient and Likelihood Ratio Tests in Cure Rate Models

2016 ◽  
Vol 5 (4) ◽  
pp. 9 ◽  
Author(s):  
Hérica P. A. Carneiro ◽  
Dione M. Valença
Keyword(s):  
Likelihood Ratio ◽  
Asymptotic Properties ◽  
Information Matrix ◽  
Survival Models ◽  
Finite Sample ◽  
Cure Rate ◽  
Time Model ◽  
Cure Rate Models ◽  
Large Sample ◽  
Finite Samples

In some survival studies part of the population may be no longer subject to the event of interest. The called cure rate models take this fact into account. They have been extensively studied for several authors who have proposed extensions and applications in real lifetime data. Classic large sample tests are usually considered in these applications, especially the likelihood ratio. Recently  a new test called \textit{gradient test} has been proposed. The gradient statistic shares the same asymptotic properties with the classic likelihood ratio and does not involve knowledge of the information matrix, which can be an advantage in survival models. Some simulation studies have been carried out to explore the behavior of the gradient test in finite samples and compare it with the classic tests in different models. However little is known about the properties of these large sample tests in finite sample for cure rate models. In this work we  performed a simulation study based on the promotion time model with Weibull distribution, to assess the performance of likelihood ratio and gradient tests in finite samples. An application is presented to illustrate the results.

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