On partially observed competing risks model for Chen distribution under generalized progressive hybrid censoring
In this paper, we discuss the inference for the competing risks model when the failure times follow Chen distribution. With assumption of two causes of failures, which are partially observed, are considered as independent. The existence and uniqueness of maximum likelihood estimates for model parame...
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| Veröffentlicht in: | Statistica Neerlandica 2024-02, Vol.78 (1), p.105-135 |
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| creator | Singh, Kundan Kumar Mahto, Amulya Mani Tripathi, Yogesh |
| description | In this paper, we discuss the inference for the competing risks model when the failure times follow Chen distribution. With assumption of two causes of failures, which are partially observed, are considered as independent. The existence and uniqueness of maximum likelihood estimates for model parameters are obtained under generalized progressive hybrid censoring. Also, we discussed the classical and Bayesian inferences of the model parameters under the assumption of restricted and nonrestricted parameters. Performance of classical point and interval estimators are compared with Bayesian point and interval estimators by conducting extensive simulation study. In addition to that, for illustration purpose, a real life example is discussed. Finally, some concluding remarks, regarding the presented model, are made. |
| doi_str_mv | 10.1111/stan.12308 |
| format | Article |
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Finally, some concluding remarks, regarding the presented model, are made.</description><subject>asymptotic confidence interval</subject><subject>Bayes estimates</subject><subject>Bayesian analysis</subject><subject>Chen distribution</subject><subject>competing risk model</subject><subject>Estimators</subject><subject>Failure times</subject><subject>generalized progressive hybrid censoring</subject><subject>Mathematical models</subject><subject>Maximum likelihood estimates</subject><subject>maximum likelihood estimation</subject><subject>MCMC algorithm</subject><subject>Parameters</subject><issn>0039-0402</issn><issn>1467-9574</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kM1OwzAQhC0EEqVw4QkscUNKsWPHcY5VxZ9U0QPlHDnxpnVJ7WAnoPD0uJQze9nLN7M7g9A1JTMa5y70ys5oyog8QRPKRZ4UWc5P0YQQViSEk_QcXYSwI4TmBRcT1K0s7pTvjWrbEbsqgP8EjWu376A3doO9Ce8B752GFjfO48UWLNYm9N5UQ2-cxYPV4PEGLHjVmu-o7rzbeAjBfALejpU30RBscD4aXqKzRrUBrv72FL093K8XT8ly9fi8mC-TmjEuEykqxRQvUiYylQoqGy1JA5mWTGipdJUWRZNBXbGs0aoWhNVNLasoq9Kcx7RTdHP0jc98DBD6cucGb-PJMpWF4FwwkkXq9kjV3oXgoSk7b_bKjyUl5aHR8tBo-dtohOkR_jItjP-Q5et6_nLU_AAsV3vf</recordid><startdate>202402</startdate><enddate>202402</enddate><creator>Singh, Kundan</creator><creator>Kumar Mahto, Amulya</creator><creator>Mani Tripathi, Yogesh</creator><general>Blackwell Publishing Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-8389-5257</orcidid></search><sort><creationdate>202402</creationdate><title>On partially observed competing risks model for Chen distribution under generalized progressive hybrid censoring</title><author>Singh, Kundan ; Kumar Mahto, Amulya ; Mani Tripathi, Yogesh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3348-86ba3a492365a2618fd80fe5d836d8adb299f5ecb35fdac603cfc8b86bb274003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>asymptotic confidence interval</topic><topic>Bayes estimates</topic><topic>Bayesian analysis</topic><topic>Chen distribution</topic><topic>competing risk model</topic><topic>Estimators</topic><topic>Failure times</topic><topic>generalized progressive hybrid censoring</topic><topic>Mathematical models</topic><topic>Maximum likelihood estimates</topic><topic>maximum likelihood estimation</topic><topic>MCMC algorithm</topic><topic>Parameters</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Singh, Kundan</creatorcontrib><creatorcontrib>Kumar Mahto, Amulya</creatorcontrib><creatorcontrib>Mani Tripathi, Yogesh</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Statistica Neerlandica</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Singh, Kundan</au><au>Kumar Mahto, Amulya</au><au>Mani Tripathi, Yogesh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On partially observed competing risks model for Chen distribution under generalized progressive hybrid censoring</atitle><jtitle>Statistica Neerlandica</jtitle><date>2024-02</date><risdate>2024</risdate><volume>78</volume><issue>1</issue><spage>105</spage><epage>135</epage><pages>105-135</pages><issn>0039-0402</issn><eissn>1467-9574</eissn><abstract>In this paper, we discuss the inference for the competing risks model when the failure times follow Chen distribution. 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| subjects | asymptotic confidence interval Bayes estimates Bayesian analysis Chen distribution competing risk model Estimators Failure times generalized progressive hybrid censoring Mathematical models Maximum likelihood estimates maximum likelihood estimation MCMC algorithm Parameters |
| title | On partially observed competing risks model for Chen distribution under generalized progressive hybrid censoring |
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