Model Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity Data

Model Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity Data

The most commonly used model selection criterion, Akaike’s Information Criterion (AIC), cannot be used when the Generalized Estimating Equations (GEE) approach is considered for analyzing multivariate binary response. Recently, a modified version of AIC (mAIC) which is based on quasi-likelihood func...

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Main Authors: A.H.M. Mahbub Latif, M. Zakir Hossain, M. Ataharul Islam
Format: Article
Language:English
Published: Austrian Statistical Society 2016-04-01
Series:Austrian Journal of Statistics
Online Access:http://www.ajs.or.at/index.php/ajs/article/view/298
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spelling doaj-ef1ddc593ab8405d8bca488af28b2ffe2021-04-22T12:34:08ZengAustrian Statistical SocietyAustrian Journal of Statistics1026-597X2016-04-0137210.17713/ajs.v37i2.298Model Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity DataA.H.M. Mahbub Latif0M. Zakir Hossain1M. Ataharul Islam2Institute of Statistical Research and Training, University of Dhaka, BangladeshDepartment of Statistics, University of Dhaka, BangladeshDepartment of Statistics, University of Dhaka, BangladeshThe most commonly used model selection criterion, Akaike’s Information Criterion (AIC), cannot be used when the Generalized Estimating Equations (GEE) approach is considered for analyzing multivariate binary response. Recently, a modified version of AIC (mAIC) which is based on quasi-likelihood function is proposed as a model selection criterion. This model selection criterion can be used in the GEE setup. In this study, an application of mAIC is showed in selecting important covariates associated with pregnancy related complications of Bangladeshi women. http://www.ajs.or.at/index.php/ajs/article/view/298
collection DOAJ
language English
format Article
sources DOAJ
author A.H.M. Mahbub Latif
M. Zakir Hossain
M. Ataharul Islam
spellingShingle A.H.M. Mahbub Latif
M. Zakir Hossain
M. Ataharul Islam
Model Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity Data
Austrian Journal of Statistics
author_facet A.H.M. Mahbub Latif
M. Zakir Hossain
M. Ataharul Islam
author_sort A.H.M. Mahbub Latif
title Model Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity Data
title_short Model Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity Data
title_full Model Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity Data
title_fullStr Model Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity Data
title_full_unstemmed Model Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity Data
title_sort model selection using modified akaike’s information criterion: an application to maternal morbidity data
publisher Austrian Statistical Society
series Austrian Journal of Statistics
issn 1026-597X
publishDate 2016-04-01
description The most commonly used model selection criterion, Akaike’s Information Criterion (AIC), cannot be used when the Generalized Estimating Equations (GEE) approach is considered for analyzing multivariate binary response. Recently, a modified version of AIC (mAIC) which is based on quasi-likelihood function is proposed as a model selection criterion. This model selection criterion can be used in the GEE setup. In this study, an application of mAIC is showed in selecting important covariates associated with pregnancy related complications of Bangladeshi women.
url http://www.ajs.or.at/index.php/ajs/article/view/298
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AT mzakirhossain modelselectionusingmodifiedakaikesinformationcriterionanapplicationtomaternalmorbiditydata
AT mataharulislam modelselectionusingmodifiedakaikesinformationcriterionanapplicationtomaternalmorbiditydata
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