Abstract
The paper aims to propose a family of estimators for the Bayesian analysis of three parametric generalized gamma (GG) distribution under different priors and loss functions. We have proposed the Gibbs sampler to obtain the numerical solutions for the point and interval estimators of the parameters using WinBugs. The comparison among the different estimators has been made in terms of posterior risks and the widths of the corresponding credible intervals. A simulation study has been conducted to investigate the performance of the estimators under different combinations of the parametric values and using various sample sizes. A real life data set has been analyzed to illustrate the practical applicability of the results.
Highlights
IntroductionThe generalized gamma (GG) distribution introduced by Stacy (1962) offers a flexible family and many of the important lifetime models (such as Weibull, gamma and exponential models) can be obtained as special cases, the model has applications in several other fields too
The generalized gamma (GG) distribution introduced by Stacy (1962) offers a flexible family and many of the important lifetime models can be obtained as special cases, the model has applications in several other fields too
The algorithm has been implemented in Splus and the results have been validated on the particular cases of the generalized gamma distribution
Summary
The generalized gamma (GG) distribution introduced by Stacy (1962) offers a flexible family and many of the important lifetime models (such as Weibull, gamma and exponential models) can be obtained as special cases, the model has applications in several other fields too. Khodabin and Ahmadabadi (2010) considered the GG distribution and presented a new moment estimation method of parameters using its characterization, MLE for gamma subfamily, entropy representation, Kullback-Leibler discrimination, Akaike and Bayesian information criterion. X and Y are distributed independently as generalized gamma distributions They considered the Bayesian analysis under exponential prior using importance sampling. Noufaily and Jones (2013) explored the computational aspects of likelihood maximization for the generalized gamma (GG) distribution. The Bayesian estimators, which utilize the cost function of the logspectral amplitude (LSA) estimator, are based on generalized Gamma distribution under speech presence probability. The likelihood function for the GG distribution using a sample of size ‘n’ can be derived as:
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Pakistan Journal of Statistics and Operation Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
