Survival mixtrue model of Gamma distribution F of modelling heterogeneous data

In this study survival mixture model of three components was proposed for the analysis of heterogeneous survival data.The proposed model constitutes of three components survival mixture model of the Gamma distribution.The properties of model were highlighted. Both simulated and real data were used...

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Main Authors: Mohammed, Yusuf A., Yatim, Bidin, Ismail, Suzilah
Format: Article
Language:English
Published: Research India Publications 2016
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Online Access:http://repo.uum.edu.my/21529/1/IJAER%2011%2016%202016%208992%208998.pdf
http://repo.uum.edu.my/21529/
http://www.ripublication.com/ijaer16/ijaerv11n16_32.pdf
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spelling my.uum.repo.215292017-04-06T04:09:28Z http://repo.uum.edu.my/21529/ Survival mixtrue model of Gamma distribution F of modelling heterogeneous data Mohammed, Yusuf A. Yatim, Bidin Ismail, Suzilah QA Mathematics In this study survival mixture model of three components was proposed for the analysis of heterogeneous survival data.The proposed model constitutes of three components survival mixture model of the Gamma distribution.The properties of model were highlighted. Both simulated and real data were used to estimate the maximum likelihood estimators of the model by employing the Expectation Maximization (EM). Three different censoring percentages (10%, 20% and 40%) were employed in the simulated data to assess the performance of the proposed model with different censoring percentages.The comparison showed that the model performed well with the three censoring percentages.However, the estimated parameters were better with small censoring percentage. The real data were used to compare the proposed model with the pure classical parametric survival models corresponding to each component, the two and four components survival mixture models of the Gamma distributions.The Log-likelihood (LL) and the Akaike Information Criterion (AIC) values showed that the proposed model represents real data better than the pure classical survival model, the two and four components survival mixture models of the Gamma distributions.The proposed model showed that survival mixture models are flexible and maintain the features of the pure classical survival model and are better option for modelling heterogeneous survival data. Research India Publications 2016 Article PeerReviewed application/pdf en http://repo.uum.edu.my/21529/1/IJAER%2011%2016%202016%208992%208998.pdf Mohammed, Yusuf A. and Yatim, Bidin and Ismail, Suzilah (2016) Survival mixtrue model of Gamma distribution F of modelling heterogeneous data. International Journal of Applied Engineering Research, 11 (16). pp. 8992-8998. ISSN 0973-4562 http://www.ripublication.com/ijaer16/ijaerv11n16_32.pdf
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Mohammed, Yusuf A.
Yatim, Bidin
Ismail, Suzilah
Survival mixtrue model of Gamma distribution F of modelling heterogeneous data
description In this study survival mixture model of three components was proposed for the analysis of heterogeneous survival data.The proposed model constitutes of three components survival mixture model of the Gamma distribution.The properties of model were highlighted. Both simulated and real data were used to estimate the maximum likelihood estimators of the model by employing the Expectation Maximization (EM). Three different censoring percentages (10%, 20% and 40%) were employed in the simulated data to assess the performance of the proposed model with different censoring percentages.The comparison showed that the model performed well with the three censoring percentages.However, the estimated parameters were better with small censoring percentage. The real data were used to compare the proposed model with the pure classical parametric survival models corresponding to each component, the two and four components survival mixture models of the Gamma distributions.The Log-likelihood (LL) and the Akaike Information Criterion (AIC) values showed that the proposed model represents real data better than the pure classical survival model, the two and four components survival mixture models of the Gamma distributions.The proposed model showed that survival mixture models are flexible and maintain the features of the pure classical survival model and are better option for modelling heterogeneous survival data.
format Article
author Mohammed, Yusuf A.
Yatim, Bidin
Ismail, Suzilah
author_facet Mohammed, Yusuf A.
Yatim, Bidin
Ismail, Suzilah
author_sort Mohammed, Yusuf A.
title Survival mixtrue model of Gamma distribution F of modelling heterogeneous data
title_short Survival mixtrue model of Gamma distribution F of modelling heterogeneous data
title_full Survival mixtrue model of Gamma distribution F of modelling heterogeneous data
title_fullStr Survival mixtrue model of Gamma distribution F of modelling heterogeneous data
title_full_unstemmed Survival mixtrue model of Gamma distribution F of modelling heterogeneous data
title_sort survival mixtrue model of gamma distribution f of modelling heterogeneous data
publisher Research India Publications
publishDate 2016
url http://repo.uum.edu.my/21529/1/IJAER%2011%2016%202016%208992%208998.pdf
http://repo.uum.edu.my/21529/
http://www.ripublication.com/ijaer16/ijaerv11n16_32.pdf
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score 13.18916