BAYESIAN PARAMETER ESTIMATION OF WEIBULL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR RANDOM CENSORING TEST MODEL WITH INCOMPLETE INFORMATION
The complete-data likelihood function of Weibull distribution with multiple change points for IIRCT is obtained by filling in missing life data using inverse transformation method. The full conditional distributions of change-point positions and other unknown parameters are obtained. Every parameter...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2016-01-01
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| Series: | Jixie qiangdu |
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| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2016.03.017 |
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| author | HE ChaoBing |
| author_facet | HE ChaoBing |
| author_sort | HE ChaoBing |
| collection | DOAJ |
| description | The complete-data likelihood function of Weibull distribution with multiple change points for IIRCT is obtained by filling in missing life data using inverse transformation method. The full conditional distributions of change-point positions and other unknown parameters are obtained. Every parameter is sampled by Gibbs sampler. and the means of Gibbs samples are taken as Bayesian estimations of the parameters. The concrete steps of MCMC methods are given. The random simulation results show that the estimations are fairly accurate and the effect is good. |
| format | Article |
| id | doaj-art-e2ea913d9da542bdb2371c66ae604365 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2016-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-e2ea913d9da542bdb2371c66ae6043652025-01-15T02:36:28ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692016-01-013852252530594984BAYESIAN PARAMETER ESTIMATION OF WEIBULL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR RANDOM CENSORING TEST MODEL WITH INCOMPLETE INFORMATIONHE ChaoBingThe complete-data likelihood function of Weibull distribution with multiple change points for IIRCT is obtained by filling in missing life data using inverse transformation method. The full conditional distributions of change-point positions and other unknown parameters are obtained. Every parameter is sampled by Gibbs sampler. and the means of Gibbs samples are taken as Bayesian estimations of the parameters. The concrete steps of MCMC methods are given. The random simulation results show that the estimations are fairly accurate and the effect is good.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2016.03.017Complete-data likelihood functionFull conditional distributionMCMC methodGibbs samplingMetropolis-Hastings algorithm |
| spellingShingle | HE ChaoBing BAYESIAN PARAMETER ESTIMATION OF WEIBULL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR RANDOM CENSORING TEST MODEL WITH INCOMPLETE INFORMATION Jixie qiangdu Complete-data likelihood function Full conditional distribution MCMC method Gibbs sampling Metropolis-Hastings algorithm |
| title | BAYESIAN PARAMETER ESTIMATION OF WEIBULL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR RANDOM CENSORING TEST MODEL WITH INCOMPLETE INFORMATION |
| title_full | BAYESIAN PARAMETER ESTIMATION OF WEIBULL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR RANDOM CENSORING TEST MODEL WITH INCOMPLETE INFORMATION |
| title_fullStr | BAYESIAN PARAMETER ESTIMATION OF WEIBULL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR RANDOM CENSORING TEST MODEL WITH INCOMPLETE INFORMATION |
| title_full_unstemmed | BAYESIAN PARAMETER ESTIMATION OF WEIBULL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR RANDOM CENSORING TEST MODEL WITH INCOMPLETE INFORMATION |
| title_short | BAYESIAN PARAMETER ESTIMATION OF WEIBULL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR RANDOM CENSORING TEST MODEL WITH INCOMPLETE INFORMATION |
| title_sort | bayesian parameter estimation of weibull distribution with multiple change points for random censoring test model with incomplete information |
| topic | Complete-data likelihood function Full conditional distribution MCMC method Gibbs sampling Metropolis-Hastings algorithm |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2016.03.017 |
| work_keys_str_mv | AT hechaobing bayesianparameterestimationofweibulldistributionwithmultiplechangepointsforrandomcensoringtestmodelwithincompleteinformation |