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/ http://irep.iium.edu.my/72207/ http://irep.iium.edu.my/72207/1/Statistical%20Modeling%20via%20Bootstrapping%20and.pdf

id	iium-72207
recordtype	eprints
spelling	iium-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 E-ISSN 1792-8036 https://www.etasr.com/index.php/ETASR/article/view/2126/pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
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/ http://irep.iium.edu.my/72207/ http://irep.iium.edu.my/72207/1/Statistical%20Modeling%20via%20Bootstrapping%20and.pdf
first_indexed	2023-09-18T21:42:22Z
last_indexed	2023-09-18T21:42:22Z
_version_	1777413224767946752

Statistical modeling via bootstrapping and weighted techniques based on variances

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