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Assessing performance of the generalized exponential model in the presence of the interval censored data with covariate

This study aims to extend the generalized exponential model (GEM) to include covariates in the presence of interval-censored data. The maximum likelihood estimator (MLE) was obtained for the parameter of the model formulated. Afterward, a thorough simulation study was carried out to evaluate the est...

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Κύριοι συγγραφείς: Alharbi, Nada, A., Jayanthi, A., Haizum, Ling, Wendy
Μορφή: Άρθρο
Έκδοση: Oesterreichische Statistische Gesellschaft (OSG) 2022
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id oai:psasir.upm.edu.my:100397
record_format eprints
spelling oai:psasir.upm.edu.my:100397 http://psasir.upm.edu.my/id/eprint/100397/ Assessing performance of the generalized exponential model in the presence of the interval censored data with covariate Alharbi, Nada A., Jayanthi A., Haizum Ling, Wendy This study aims to extend the generalized exponential model (GEM) to include covariates in the presence of interval-censored data. The maximum likelihood estimator (MLE) was obtained for the parameter of the model formulated. Afterward, a thorough simulation study was carried out to evaluate the estimator's performance based on the values of bias, standard error (SE), and root mean square error (RMSE). The result indicated that the (SE) and (RMSE) decrease with the increase in sample sizes and decrease in censoring proportions. Finally, the performance of the Wald confidence interval estimation technique for the GE model with interval-censored data covariate was assessed by a coverage probability study at several censoring proportions and different sample sizes. Oesterreichische Statistische Gesellschaft (OSG) 2022-01-24 Article PeerReviewed Alharbi, Nada and A., Jayanthi and A., Haizum and Ling, Wendy (2022) Assessing performance of the generalized exponential model in the presence of the interval censored data with covariate. Austrian Journal of Statistics, 51 (1). 52 - 69. ISSN 1026-597X https://www.ajs.or.at/index.php/ajs/article/view/1192 10.17713/ajs.v51i1.1192
institution UPM IR
collection UPM IR
description This study aims to extend the generalized exponential model (GEM) to include covariates in the presence of interval-censored data. The maximum likelihood estimator (MLE) was obtained for the parameter of the model formulated. Afterward, a thorough simulation study was carried out to evaluate the estimator's performance based on the values of bias, standard error (SE), and root mean square error (RMSE). The result indicated that the (SE) and (RMSE) decrease with the increase in sample sizes and decrease in censoring proportions. Finally, the performance of the Wald confidence interval estimation technique for the GE model with interval-censored data covariate was assessed by a coverage probability study at several censoring proportions and different sample sizes.
format Article
author Alharbi, Nada
A., Jayanthi
A., Haizum
Ling, Wendy
spellingShingle Alharbi, Nada
A., Jayanthi
A., Haizum
Ling, Wendy
Assessing performance of the generalized exponential model in the presence of the interval censored data with covariate
author_facet Alharbi, Nada
A., Jayanthi
A., Haizum
Ling, Wendy
author_sort Alharbi, Nada
title Assessing performance of the generalized exponential model in the presence of the interval censored data with covariate
title_short Assessing performance of the generalized exponential model in the presence of the interval censored data with covariate
title_full Assessing performance of the generalized exponential model in the presence of the interval censored data with covariate
title_fullStr Assessing performance of the generalized exponential model in the presence of the interval censored data with covariate
title_full_unstemmed Assessing performance of the generalized exponential model in the presence of the interval censored data with covariate
title_sort assessing performance of the generalized exponential model in the presence of the interval censored data with covariate
publisher Oesterreichische Statistische Gesellschaft (OSG)
publishDate 2022
_version_ 1819301183492194304
score 13.4562235

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