Cover Image

Model-building of multiple binary logit using model averaging

Many researchers had been carried out on the study of statistical modelling, making it easier for new researchers in many sectors (social sciences, economics, medical, and etc.) to obtain knowledge in order to ease their research study. Nevertheless, there is still no agreed guidelines in obtaining...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohd Padzil, Siti Aisyah, Pillay, Khuneswari Gopal, Mohd Salleh, Rohayu
Format: Article
Published: Science Publishing Corporation 2018
Subjects:
QA273-280 Probabilities. Mathematical statistics
Online Access:http://eprints.uthm.edu.my/4407/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uthm.eprints.4407
record_format eprints
spelling my.uthm.eprints.44072021-12-02T07:18:32Z http://eprints.uthm.edu.my/4407/ Model-building of multiple binary logit using model averaging Mohd Padzil, Siti Aisyah Pillay, Khuneswari Gopal Mohd Salleh, Rohayu QA273-280 Probabilities. Mathematical statistics Many researchers had been carried out on the study of statistical modelling, making it easier for new researchers in many sectors (social sciences, economics, medical, and etc.) to obtain knowledge in order to ease their research study. Nevertheless, there is still no agreed guidelines in obtaining the best model for multiple binary logit (MBL) using model averaging (MA). This research will demonstrate the proper guidelines to obtain best MBL model by using MA. Upper Gastrointestinal Bleed (UGIB) data were studied to illustrate the process of model-building using the proposed guidelines. This study will pinpoint the factors with high possibility leading to mortality of UGIB patients using obtained best model. Corrected Akaike Information Criteria (AICc) and Bayesian Information Criteria (BIC) were used to compute the weights in model averaging method. The performance of the models was computed by using Root mean square error (RMSE) and mean absolute error (MAE). Model obtained by using BIC weights showed a better performance since the RMSE and MAE values are lower compared to model obtained using AICc weights. The factors that affects the survivability of UGIB patients are shock score, comorbidity and rebleed. In conclusion, model-building of multiple binary logit using model averaging showed a better performance when using BIC. Science Publishing Corporation 2018 Article PeerReviewed Mohd Padzil, Siti Aisyah and Pillay, Khuneswari Gopal and Mohd Salleh, Rohayu (2018) Model-building of multiple binary logit using model averaging. International Journal of Engineering and Technology, 7 (4.3). pp. 224-228. ISSN 2227-524X
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
topic QA273-280 Probabilities. Mathematical statistics
spellingShingle QA273-280 Probabilities. Mathematical statistics
Mohd Padzil, Siti Aisyah
Pillay, Khuneswari Gopal
Mohd Salleh, Rohayu
Model-building of multiple binary logit using model averaging
description Many researchers had been carried out on the study of statistical modelling, making it easier for new researchers in many sectors (social sciences, economics, medical, and etc.) to obtain knowledge in order to ease their research study. Nevertheless, there is still no agreed guidelines in obtaining the best model for multiple binary logit (MBL) using model averaging (MA). This research will demonstrate the proper guidelines to obtain best MBL model by using MA. Upper Gastrointestinal Bleed (UGIB) data were studied to illustrate the process of model-building using the proposed guidelines. This study will pinpoint the factors with high possibility leading to mortality of UGIB patients using obtained best model. Corrected Akaike Information Criteria (AICc) and Bayesian Information Criteria (BIC) were used to compute the weights in model averaging method. The performance of the models was computed by using Root mean square error (RMSE) and mean absolute error (MAE). Model obtained by using BIC weights showed a better performance since the RMSE and MAE values are lower compared to model obtained using AICc weights. The factors that affects the survivability of UGIB patients are shock score, comorbidity and rebleed. In conclusion, model-building of multiple binary logit using model averaging showed a better performance when using BIC.
format Article
author Mohd Padzil, Siti Aisyah
Pillay, Khuneswari Gopal
Mohd Salleh, Rohayu
author_facet Mohd Padzil, Siti Aisyah
Pillay, Khuneswari Gopal
Mohd Salleh, Rohayu
author_sort Mohd Padzil, Siti Aisyah
title Model-building of multiple binary logit using model averaging
title_short Model-building of multiple binary logit using model averaging
title_full Model-building of multiple binary logit using model averaging
title_fullStr Model-building of multiple binary logit using model averaging
title_full_unstemmed Model-building of multiple binary logit using model averaging
title_sort model-building of multiple binary logit using model averaging
publisher Science Publishing Corporation
publishDate 2018
url http://eprints.uthm.edu.my/4407/
_version_ 1738581246720606208
score 13.211869

Similar Items