Please use this identifier to cite or link to this item: https://doi.org/10.1080/02664760801920846
Title: Markov chain Monte Carlo methods for parameter estimation of the modified Weibull distribution
Authors: Jiang, H.
Xie, M. 
Tang, L.C. 
Keywords: Adaptive rejection sampling
Gibbs sampler
Markov chain Monte Carlo
Maximum likelihood
Modified Weibull distribution
Probability interval
Issue Date: Jun-2008
Source: Jiang, H., Xie, M., Tang, L.C. (2008-06). Markov chain Monte Carlo methods for parameter estimation of the modified Weibull distribution. Journal of Applied Statistics 35 (6) : 647-658. ScholarBank@NUS Repository. https://doi.org/10.1080/02664760801920846
Abstract: In this paper, the Markov chain Monte Carlo (MCMC) method is used to estimate the parameters of a modified Weibull distribution based on a complete sample. While maximum-likelihood estimation (MLE) is the most used method for parameter estimation, MCMC has recently emerged as a good alternative. When applied to parameter estimation, MCMC methods have been shown to be easy to implement computationally, the estimates always exist and are statistically consistent, and their probability intervals are convenient to construct. Details of applying MCMC to parameter estimation for the modified Weibull model are elaborated and a numerical example is presented to illustrate the methods of inference discussed in this paper. To compare MCMC with MLE, a simulation study is provided, and the differences between the estimates obtained by the two algorithms are examined.
Source Title: Journal of Applied Statistics
URI: http://scholarbank.nus.edu.sg/handle/10635/63176
ISSN: 02664763
DOI: 10.1080/02664760801920846
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