Modelling of Cause Specific Hazard Regression using Rayleigh Distribution with Bayesian Estimation under Non-Informative Priors

By : Habbiburr Rehman , Navin Chandra , Aquil Ahmed , Anupam Kumar

Page No: 93-107

Abstract
In this article, we considered the problem of survival analysis with competing causes of failure. For analysing competing risks survival data, we used parametric cause specific hazard regression model by assuming Rayleigh distribution as baseline model. The estimates of unknown parameters and cumulative cause specific hazard function are obtained through maximum likelihood as well as Bayesian methods of estimation. The Bayes estimates are obtained based on non-informative prior under two types of loss functions such as squared error loss function and LINEX loss function. For the comprehensive comparison of methods of estimation, we conduct a Monte Carlo simulation study. For the validation of proposed model, a bone marrow transplant real data is considered.

Authors :
Habbiburr Rehman : Department of Statistics, Ramanujan School of Mathematical Sciences, Pondicherry University, Puducherry, India
Navin Chandra : Department of Statistics, Ramanujan School of Mathematical Sciences, Pondicherry University, Puducherry, India
Aquil Ahmed : Department of Statistics & Operations Research, Aligarh Muslim University, India
Anupam Kumar : Department of Statistics, PUC, Mizoram University, Aizawl, India
 

DOI: https://doi.org/10.32381/IAPQRT.2024.48.01-02.5