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Master's Dissertation
DOI
https://doi.org/10.11606/D.104.2021.tde-27052021-112631
Document
Author
Full name
Renata Cristina Carregari
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2021
Supervisor
Committee
Suzuki, Adriano Kamimura (President)
Silva, Paulo Henrique Ferreira da
Tojeiro, Cynthia Arantes Vieira
Title in Portuguese
Um novo modelo de sobrevivência Bell-Inversa Gaussiana com fração de cura
Keywords in Portuguese
Análise de sobrevivência
Distribuição Bell
Distribuição Inversa Gaussiana
Esquema de ativação latente
Modelo de sobrevivência com fração de cura
Abstract in Portuguese
Neste trabalho propomos um novo modelo de sobrevivência denominado Bell-Inversa Gaussiana com fração de cura. Consideramos diferentes esquemas de ativação em que o número de fatores M tem a distribuição Bell e o tempo de ocorrência de um evento segue o modelo Inversa Gaussiana. Os parâmetros são estimados pelos métodos clássico e Bayesiano. Em um estudo de simulação, investigamos as médias das estimativas, os vieses, os erros quadráticos médios e as probabilidades de cobertura nos diferentes esquemas de ativação. Com o objetivo de detectar possíveis observações influentes ou extremas que podem causar distorções nos resultados da análise, utilizamos o método Bayesiano de análise de influência de deleção de casos baseado na divergência ψ. Por fim, mostramos a aplicabilidade do modelo proposto a um conjunto de dados reais.
Title in English
A new Bell Inverse Gaussian cure rate survival model
Keywords in English
Bell distribution
Cure rate survival model
Inverse Gaussian distribution
Latentactivation schemes
Survival analysis
Abstract in English
In this work we propose a new survival model called the Bell-Inverse Gaussian cure rate. We consider different activation schemes in which the number of factors M has the Bell distribution and the time of occurrence of an event follows the Inverse Gaussian model. The parameters are estimated by the classical and Bayesian methods. In a simulation study, we investigate the mean estimates, biases, mean squared errors and coverage probabilities in different activation schemes. In order to detect possible influential or extreme observations that can cause distortions on the results of the analysis we use the Bayesian method of influence analysis of case deletion based on ψ-divergence. Finally, we show the applicability of the proposed model to a real datase
 
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Publishing Date
2021-05-27
 
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