Bayesian Inference of the Weibull-Pareto Distribution
Location
Atrium
Session Format
Poster Presentation
Research Area Topic:
Natural & Physical Sciences - Mathematics
Co-Presenters and Faculty Mentors or Advisors
Arpita Chatterjee - co-author/ faculty advisor
Abstract
Bayesian Inference of the Weibull-Pareto Distribution
Weibull distribution has been extensively used over the past decades for modeling lifetime data. It has intensive application in the field of biological studies, reliability engineering, general insurance, electrical engineering, and industrial engineering. Recently, a new distribution, namely, Weibull-Pareto distribution has been introduced in the literature. Depending on the value of its parameters, this distribution can be implemented to model highly skewed (left or right) data. This type of skewed distribution is typical when dealing with survival and actuarial data. In this paper we introduce a hierarchical Bayesian model for estimating parameters of the Weibull-Pareto distribution. We introduce appropriate prior distributions for the model parameters. Markov Chain Monte Carlo (MCMC) methods are then used to develop Bayesian inference for the proposed model. This model is illustrated by survival data from patients diagnosed with cutaneous melanoma. We further compare the performance of the proposed model with other competing models in simulated data sets.
Keywords: Weibull-Pareto, hierarchical Bayesian model, right censored, survival data, MCMC.
Keywords
Weibull-Pareto, Hierarchical Bayesian model, Right censored, Survival data, MCMC
Presentation Type and Release Option
Presentation (Open Access)
Start Date
4-24-2015 2:45 PM
End Date
4-24-2015 4:00 PM
Recommended Citation
Dow, James, "Bayesian Inference of the Weibull-Pareto Distribution" (2015). GS4 Georgia Southern Student Scholars Symposium. 109.
https://digitalcommons.georgiasouthern.edu/research_symposium/2015/2015/109
Bayesian Inference of the Weibull-Pareto Distribution
Atrium
Bayesian Inference of the Weibull-Pareto Distribution
Weibull distribution has been extensively used over the past decades for modeling lifetime data. It has intensive application in the field of biological studies, reliability engineering, general insurance, electrical engineering, and industrial engineering. Recently, a new distribution, namely, Weibull-Pareto distribution has been introduced in the literature. Depending on the value of its parameters, this distribution can be implemented to model highly skewed (left or right) data. This type of skewed distribution is typical when dealing with survival and actuarial data. In this paper we introduce a hierarchical Bayesian model for estimating parameters of the Weibull-Pareto distribution. We introduce appropriate prior distributions for the model parameters. Markov Chain Monte Carlo (MCMC) methods are then used to develop Bayesian inference for the proposed model. This model is illustrated by survival data from patients diagnosed with cutaneous melanoma. We further compare the performance of the proposed model with other competing models in simulated data sets.
Keywords: Weibull-Pareto, hierarchical Bayesian model, right censored, survival data, MCMC.
