A new biparametric survival model: classical and Bayesian inference

• Grant number: 21/02301-4
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2021
• End date: April 30, 2022
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Statistics
• Principal Investigator: Adriano Kamimura Suzuki
• Grantee: Gabriel Castro Gulin Rosa
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

In this work, we propose a new survival model based on the generalized logarithmic transformation method to model complete data or in the presence of censored observations. We will assume A model as the baseline distribution and, for inferential parameters, we will perform a Classic approach using maximum likelihood estimation and a Bayesian approach using Markov chain Monte Carlo (MCMC).We will show its applicability to simulated and real data sets. All implementations computational will be performed using the JAGS and R systems.