The Bayesian Approach to Progressive Type-I Interval Censoring Data under Generalized Gamma Distribution

The Bayesian Approach to Progressive Type-I Interval Censoring Data under Generalized Gamma Distribution

碩士 === 中原大學 === 應用數學研究所 === 99 === In the survival analysis, the experimental changes may not be continuously observed all the time.Hence complete observations are sometimes not available.In practice, only censored interval data can be obtained. In this research, we assume data come from the general...

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Main Authors: Szu-Wei Chu, 褚思暐
Other Authors: Yu-Jau Lin
Format: Others
Language:zh-TW
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/98630161587575559098
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spelling ndltd-TW-099CYCU55070802015-10-13T20:23:26Z http://ndltd.ncl.edu.tw/handle/98630161587575559098 The Bayesian Approach to Progressive Type-I Interval Censoring Data under Generalized Gamma Distribution 廣義伽瑪分配逐步型一區間設限資料之貝氏分析 Szu-Wei Chu 褚思暐 碩士 中原大學 應用數學研究所 99 In the survival analysis, the experimental changes may not be continuously observed all the time.Hence complete observations are sometimes not available.In practice, only censored interval data can be obtained. In this research, we assume data come from the generalized gamma distribution and they are collected in progressive type-I interval ensoring. We then apply Bayesian analysis via MCMC to do the statistical estimation. Simulation studies, along with the mean square errors of parameters of interest, are shown.Moreover, we analyze the real data set, Carbone et al.(1967), and compare the results with previously done MLE and EM methods. Yu-Jau Lin 林余昭 2011 學位論文 ; thesis 33 zh-TW
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description 碩士 === 中原大學 === 應用數學研究所 === 99 === In the survival analysis, the experimental changes may not be continuously observed all the time.Hence complete observations are sometimes not available.In practice, only censored interval data can be obtained. In this research, we assume data come from the generalized gamma distribution and they are collected in progressive type-I interval ensoring. We then apply Bayesian analysis via MCMC to do the statistical estimation. Simulation studies, along with the mean square errors of parameters of interest, are shown.Moreover, we analyze the real data set, Carbone et al.(1967), and compare the results with previously done MLE and EM methods.
author2 Yu-Jau Lin
author_facet Yu-Jau Lin
Szu-Wei Chu
褚思暐
author Szu-Wei Chu
褚思暐
spellingShingle Szu-Wei Chu
褚思暐
The Bayesian Approach to Progressive Type-I Interval Censoring Data under Generalized Gamma Distribution
author_sort Szu-Wei Chu
title The Bayesian Approach to Progressive Type-I Interval Censoring Data under Generalized Gamma Distribution
title_short The Bayesian Approach to Progressive Type-I Interval Censoring Data under Generalized Gamma Distribution
title_full The Bayesian Approach to Progressive Type-I Interval Censoring Data under Generalized Gamma Distribution
title_fullStr The Bayesian Approach to Progressive Type-I Interval Censoring Data under Generalized Gamma Distribution
title_full_unstemmed The Bayesian Approach to Progressive Type-I Interval Censoring Data under Generalized Gamma Distribution
title_sort bayesian approach to progressive type-i interval censoring data under generalized gamma distribution
publishDate 2011
url http://ndltd.ncl.edu.tw/handle/98630161587575559098
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