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Statistical modeling via bootstrapping and weighted techniques based on variances

Multiple logistic regression is a methodology of handling dependent variables with a binary outcome. This method is becoming increasingly widespread as a statistical technique that represents a discrete probability model. Many studies have focused on the application but less on the methodology buil...

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Main Authors: Wan Ahmad, Wan Muhamad Amir, Aleng, Nor Azlida, Ali, Z, Mohd Ibrahim, Mohamad Shafiq
Format: Article
Language:English
Published: Engineering, Technology & Applied Science Research 2018
Subjects:
QA Mathematics
QA276 Mathematical Statistics
Online Access:http://irep.iium.edu.my/72207/1/Statistical%20Modeling%20via%20Bootstrapping%20and.pdf
http://irep.iium.edu.my/72207/
https://www.etasr.com/index.php/ETASR/article/view/2126/pdf
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spelling my.iium.irep.722072019-05-16T05:48:38Z http://irep.iium.edu.my/72207/ Statistical modeling via bootstrapping and weighted techniques based on variances Wan Ahmad, Wan Muhamad Amir Aleng, Nor Azlida Ali, Z Mohd Ibrahim, Mohamad Shafiq QA Mathematics QA276 Mathematical Statistics Multiple logistic regression is a methodology of handling dependent variables with a binary outcome. This method is becoming increasingly widespread as a statistical technique that represents a discrete probability model. Many studies have focused on the application but less on the methodology building. This study aims to provide an applied method for multiple logistic regression which is called modified Bayesian logistic regression modeling as an alternative technique for logistic regression analysis that focuses on a combination of the bootstrap method using SAS macro and weighted techniques based on variances using SAS algorithm. Data on oral cancer were applied to illustrate a real scenario of oral health data. This data will be applied to the multiple logistic regression algorithm and modified Bayesian logistic regression. Results from both cases are strongly supported by clinical studies. Through the proposed algorithm, the researcher will have an option whether to analyze the data with the usual or an alternative method. Final results indicate that the modified procedure can provide more efficient results especially for the case which involves statistical inferences. Engineering, Technology & Applied Science Research 2018-08 Article PeerReviewed application/pdf en http://irep.iium.edu.my/72207/1/Statistical%20Modeling%20via%20Bootstrapping%20and.pdf Wan Ahmad, Wan Muhamad Amir and Aleng, Nor Azlida and Ali, Z and Mohd Ibrahim, Mohamad Shafiq (2018) Statistical modeling via bootstrapping and weighted techniques based on variances. Engineering, Technology & Applied Science Research, 8 (4). pp. 3135-3140. ISSN 2241-4487 https://www.etasr.com/index.php/ETASR/article/view/2126/pdf
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic QA Mathematics
QA276 Mathematical Statistics
spellingShingle QA Mathematics
QA276 Mathematical Statistics
Wan Ahmad, Wan Muhamad Amir
Aleng, Nor Azlida
Ali, Z
Mohd Ibrahim, Mohamad Shafiq
Statistical modeling via bootstrapping and weighted techniques based on variances
description Multiple logistic regression is a methodology of handling dependent variables with a binary outcome. This method is becoming increasingly widespread as a statistical technique that represents a discrete probability model. Many studies have focused on the application but less on the methodology building. This study aims to provide an applied method for multiple logistic regression which is called modified Bayesian logistic regression modeling as an alternative technique for logistic regression analysis that focuses on a combination of the bootstrap method using SAS macro and weighted techniques based on variances using SAS algorithm. Data on oral cancer were applied to illustrate a real scenario of oral health data. This data will be applied to the multiple logistic regression algorithm and modified Bayesian logistic regression. Results from both cases are strongly supported by clinical studies. Through the proposed algorithm, the researcher will have an option whether to analyze the data with the usual or an alternative method. Final results indicate that the modified procedure can provide more efficient results especially for the case which involves statistical inferences.
format Article
author Wan Ahmad, Wan Muhamad Amir
Aleng, Nor Azlida
Ali, Z
Mohd Ibrahim, Mohamad Shafiq
author_facet Wan Ahmad, Wan Muhamad Amir
Aleng, Nor Azlida
Ali, Z
Mohd Ibrahim, Mohamad Shafiq
author_sort Wan Ahmad, Wan Muhamad Amir
title Statistical modeling via bootstrapping and weighted techniques based on variances
title_short Statistical modeling via bootstrapping and weighted techniques based on variances
title_full Statistical modeling via bootstrapping and weighted techniques based on variances
title_fullStr Statistical modeling via bootstrapping and weighted techniques based on variances
title_full_unstemmed Statistical modeling via bootstrapping and weighted techniques based on variances
title_sort statistical modeling via bootstrapping and weighted techniques based on variances
publisher Engineering, Technology & Applied Science Research
publishDate 2018
url http://irep.iium.edu.my/72207/1/Statistical%20Modeling%20via%20Bootstrapping%20and.pdf
http://irep.iium.edu.my/72207/
https://www.etasr.com/index.php/ETASR/article/view/2126/pdf
_version_ 1643620110526578688
score 13.2442

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