EM Algorithm for Mixture Distributions Model with Type-I Hybrid Censoring Scheme
An expectation–maximization (EM) likelihood estimation procedure is proposed to obtain the maximum likelihood estimates of the parameters in a mixture distributions model based on type-I hybrid censored samples when the mixture proportions are unknown. Three bootstrap methods are applied to construc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/19/2483 |
| _version_ | 1827680762254589952 |
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| author | Tzong-Ru Tsai Yuhlong Lio Wei-Chen Ting |
| author_facet | Tzong-Ru Tsai Yuhlong Lio Wei-Chen Ting |
| author_sort | Tzong-Ru Tsai |
| collection | DOAJ |
| description | An expectation–maximization (EM) likelihood estimation procedure is proposed to obtain the maximum likelihood estimates of the parameters in a mixture distributions model based on type-I hybrid censored samples when the mixture proportions are unknown. Three bootstrap methods are applied to construct the confidence intervals of the model parameters. Monte Carlo simulations are conducted to evaluate the performance of the proposed methods. Simulation results show that the proposed methods can perform well to obtain reliable point and interval estimation results. Three examples are used to illustrate the applications of the proposed methods. |
| first_indexed | 2024-03-10T06:55:09Z |
| format | Article |
| id | doaj.art-95d0c7bcf5a8434e8a43b31bacf83814 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T06:55:09Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-95d0c7bcf5a8434e8a43b31bacf838142023-11-22T16:31:00ZengMDPI AGMathematics2227-73902021-10-01919248310.3390/math9192483EM Algorithm for Mixture Distributions Model with Type-I Hybrid Censoring SchemeTzong-Ru Tsai0Yuhlong Lio1Wei-Chen Ting2Department of Statistics, Tamkang University, Tamsui, New Taipei City 251301, TaiwanDepartment of Mathematical Sciences, University of South Dakota, Vermillion, SD 57069, USADepartment of Statistics, Tamkang University, Tamsui, New Taipei City 251301, TaiwanAn expectation–maximization (EM) likelihood estimation procedure is proposed to obtain the maximum likelihood estimates of the parameters in a mixture distributions model based on type-I hybrid censored samples when the mixture proportions are unknown. Three bootstrap methods are applied to construct the confidence intervals of the model parameters. Monte Carlo simulations are conducted to evaluate the performance of the proposed methods. Simulation results show that the proposed methods can perform well to obtain reliable point and interval estimation results. Three examples are used to illustrate the applications of the proposed methods.https://www.mdpi.com/2227-7390/9/19/2483bootstrap methodEM algorithmmaximum likelihood estimationmixture distributions modelMonte Carlo simulation |
| spellingShingle | Tzong-Ru Tsai Yuhlong Lio Wei-Chen Ting EM Algorithm for Mixture Distributions Model with Type-I Hybrid Censoring Scheme Mathematics bootstrap method EM algorithm maximum likelihood estimation mixture distributions model Monte Carlo simulation |
| title | EM Algorithm for Mixture Distributions Model with Type-I Hybrid Censoring Scheme |
| title_full | EM Algorithm for Mixture Distributions Model with Type-I Hybrid Censoring Scheme |
| title_fullStr | EM Algorithm for Mixture Distributions Model with Type-I Hybrid Censoring Scheme |
| title_full_unstemmed | EM Algorithm for Mixture Distributions Model with Type-I Hybrid Censoring Scheme |
| title_short | EM Algorithm for Mixture Distributions Model with Type-I Hybrid Censoring Scheme |
| title_sort | em algorithm for mixture distributions model with type i hybrid censoring scheme |
| topic | bootstrap method EM algorithm maximum likelihood estimation mixture distributions model Monte Carlo simulation |
| url | https://www.mdpi.com/2227-7390/9/19/2483 |
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