Applying BBPSO Algorithm to Estimate the Weibull Parameters for Interval Data

Applying BBPSO Algorithm to Estimate the Weibull Parameters for Interval Data

碩士 === 國立臺灣科技大學 === 工業管理系 === 100 === In survival analysis, the inspection costs should be concerned. An interval data is widely used in lifetime data analysis. In this article, we present maximum likelihood estimation via Bare Bones Particle Swarm Optimization (BBPSO) algorithm to estimate two para...

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Main Authors: Chien-Ping Tang, 湯健平
Other Authors: Fu-kwun Wang
Format: Others
Language:zh-TW
Published: 2012
Online Access:http://ndltd.ncl.edu.tw/handle/63848771088336646450
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spelling ndltd-TW-100NTUS50410302015-10-13T21:17:25Z http://ndltd.ncl.edu.tw/handle/63848771088336646450 Applying BBPSO Algorithm to Estimate the Weibull Parameters for Interval Data 應用BBPSO演算法於韋伯分配之區間資料下的參數估計 Chien-Ping Tang 湯健平 碩士 國立臺灣科技大學 工業管理系 100 In survival analysis, the inspection costs should be concerned. An interval data is widely used in lifetime data analysis. In this article, we present maximum likelihood estimation via Bare Bones Particle Swarm Optimization (BBPSO) algorithm to estimate two parameters of Weibull distribution under interval censored data. This approach can produce more accuracy of the parameter estimation for the Weibull distribution. Additionally, the confidence intervals for the estimators are obtained. Compare to the mid-point method and the EM algorithm, it shows that the maximum likelihood estimates based on BBPSO algorithm perform better. Fu-kwun Wang 王福琨 2012 學位論文 ; thesis 50 zh-TW
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language zh-TW
format Others
sources NDLTD
description 碩士 === 國立臺灣科技大學 === 工業管理系 === 100 === In survival analysis, the inspection costs should be concerned. An interval data is widely used in lifetime data analysis. In this article, we present maximum likelihood estimation via Bare Bones Particle Swarm Optimization (BBPSO) algorithm to estimate two parameters of Weibull distribution under interval censored data. This approach can produce more accuracy of the parameter estimation for the Weibull distribution. Additionally, the confidence intervals for the estimators are obtained. Compare to the mid-point method and the EM algorithm, it shows that the maximum likelihood estimates based on BBPSO algorithm perform better.
author2 Fu-kwun Wang
author_facet Fu-kwun Wang
Chien-Ping Tang
湯健平
author Chien-Ping Tang
湯健平
spellingShingle Chien-Ping Tang
湯健平
Applying BBPSO Algorithm to Estimate the Weibull Parameters for Interval Data
author_sort Chien-Ping Tang
title Applying BBPSO Algorithm to Estimate the Weibull Parameters for Interval Data
title_short Applying BBPSO Algorithm to Estimate the Weibull Parameters for Interval Data
title_full Applying BBPSO Algorithm to Estimate the Weibull Parameters for Interval Data
title_fullStr Applying BBPSO Algorithm to Estimate the Weibull Parameters for Interval Data
title_full_unstemmed Applying BBPSO Algorithm to Estimate the Weibull Parameters for Interval Data
title_sort applying bbpso algorithm to estimate the weibull parameters for interval data
publishDate 2012
url http://ndltd.ncl.edu.tw/handle/63848771088336646450
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AT tāngjiànpíng yīngyòngbbpsoyǎnsuànfǎyúwéibófēnpèizhīqūjiānzīliàoxiàdecānshùgūjì
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