Inferential procedures based on the double bootstrap for log logistic regression model with censored data
Traditional inferential procedures based on the asymptotic normality assumption such as the Wald often produce misleading inferences when dealing with censored data and small samples. Alternative estimation techniques such as the jackknife and bootstrap percentile allow us to construct the interval...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Science, University of Malaya
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/51966/1/Inferential%20procedures%20based%20on%20the%20double%20bootstrap%20for%20log%20logistic%20regression%20model%20with%20censored%20data.pdf |
| _version_ | 1825930632672313344 |
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| author | Loh, Yue Fang Arasan, Jayanthi Midi, Habshah Abu Bakar, Mohd Rizam |
| author_facet | Loh, Yue Fang Arasan, Jayanthi Midi, Habshah Abu Bakar, Mohd Rizam |
| author_sort | Loh, Yue Fang |
| collection | UPM |
| description | Traditional inferential procedures based on the asymptotic normality assumption such as the Wald often produce misleading inferences when dealing with censored data and small samples. Alternative estimation techniques such as the jackknife and bootstrap percentile allow us to construct the interval estimates without relying on any classical assumptions. Recently, the double bootstrap became preferable as it is not only free from any classical assumptions, but also has higher order of accuracy. In this paper, we compare the performances of the interval estimates based on the double bootstrap without pivot with the Wald, jackknife and bootstrap percentile interval estimates for the parameters of the log logistic model with right censored data and covariates via a coverage probability study. Based on the results of the study, we concluded that the double bootstrap without pivot technique works better than the other interval estimation techniques, even when sample size is 25. The double bootstrap without pivot technique worked well with real data on hypernephroma patients. |
| first_indexed | 2024-03-06T09:14:31Z |
| format | Article |
| id | upm.eprints-51966 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:14:31Z |
| publishDate | 2015 |
| publisher | Faculty of Science, University of Malaya |
| record_format | dspace |
| spelling | upm.eprints-519662017-05-04T04:43:43Z http://psasir.upm.edu.my/id/eprint/51966/ Inferential procedures based on the double bootstrap for log logistic regression model with censored data Loh, Yue Fang Arasan, Jayanthi Midi, Habshah Abu Bakar, Mohd Rizam Traditional inferential procedures based on the asymptotic normality assumption such as the Wald often produce misleading inferences when dealing with censored data and small samples. Alternative estimation techniques such as the jackknife and bootstrap percentile allow us to construct the interval estimates without relying on any classical assumptions. Recently, the double bootstrap became preferable as it is not only free from any classical assumptions, but also has higher order of accuracy. In this paper, we compare the performances of the interval estimates based on the double bootstrap without pivot with the Wald, jackknife and bootstrap percentile interval estimates for the parameters of the log logistic model with right censored data and covariates via a coverage probability study. Based on the results of the study, we concluded that the double bootstrap without pivot technique works better than the other interval estimation techniques, even when sample size is 25. The double bootstrap without pivot technique worked well with real data on hypernephroma patients. Faculty of Science, University of Malaya 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/51966/1/Inferential%20procedures%20based%20on%20the%20double%20bootstrap%20for%20log%20logistic%20regression%20model%20with%20censored%20data.pdf Loh, Yue Fang and Arasan, Jayanthi and Midi, Habshah and Abu Bakar, Mohd Rizam (2015) Inferential procedures based on the double bootstrap for log logistic regression model with censored data. Malaysian Journal of Science, 34 (2). pp. 199-207. ISSN 0126-7906 http://e-journal.um.edu.my/publish/MJS/925-1141 |
| spellingShingle | Loh, Yue Fang Arasan, Jayanthi Midi, Habshah Abu Bakar, Mohd Rizam Inferential procedures based on the double bootstrap for log logistic regression model with censored data |
| title | Inferential procedures based on the double bootstrap for log logistic regression model with censored data |
| title_full | Inferential procedures based on the double bootstrap for log logistic regression model with censored data |
| title_fullStr | Inferential procedures based on the double bootstrap for log logistic regression model with censored data |
| title_full_unstemmed | Inferential procedures based on the double bootstrap for log logistic regression model with censored data |
| title_short | Inferential procedures based on the double bootstrap for log logistic regression model with censored data |
| title_sort | inferential procedures based on the double bootstrap for log logistic regression model with censored data |
| url | http://psasir.upm.edu.my/id/eprint/51966/1/Inferential%20procedures%20based%20on%20the%20double%20bootstrap%20for%20log%20logistic%20regression%20model%20with%20censored%20data.pdf |
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