A Simulation Study to Investigate the Performance of Survival Mixture Model of Three Different Distributions to Anlayze Heterogeneous Data

Authors

  • Yusuf Abbakar Mohammed Deptartment of Mathematical Sciences, Faculty of Sciences, University of Maiduguri, Nigeria.
  • Bidin Yatim School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Malaysia.
  • Suzilah Ismail School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Malaysia.

DOI:

https://doi.org/10.9734/bpi/ctmcs/v1/9594D

Keywords:

Survival time analysis, Maximum likelihood, EM-Algorithm, mixture model, simulation, Exponential Distribution, Gamma Distribution, Weibull Distribution

Abstract

In this paper a simulation study of a parametric mixture model of three different distributions is considered to model heterogeneous survival data. Some properties of the proposed parametric mixture of Exponential, Gamma and Weibull are investigated. The Expectation Maximization Algorithm (EM) is implemented to estimate the maximum likelihood estimators of three different postulated parametric mixture model parameters. The simulations are performed by simulating data sampled from a population of three component parametric mixture of three different distributions, and the simulations are repeated 10, 30, 50, 100 and 500 times to investigate the consistency and stability of the EM scheme. The EM Algorithm scheme developed is able to estimate the parameters of the mixture which are very close to the parameters of the postulated model.The repetitions of the simulation give parameters closer and closer to the postulated models, as the number of repetitions increases, with relatively small standard errors. The results revealed that the EM successfully estimated the parameters of the three component mixture model.

Published

2021-05-26

How to Cite

Yusuf Abbakar Mohammed, Bidin Yatim, & Suzilah Ismail. (2021). A Simulation Study to Investigate the Performance of Survival Mixture Model of Three Different Distributions to Anlayze Heterogeneous Data. Current Topics on Mathematics and Computer Science Vol. 1, 136–145. https://doi.org/10.9734/bpi/ctmcs/v1/9594D