Abstract

Classical linear regression is inadequate when the response variable is the number of success in a sequence of experiments. However, the binomial regression is considered more suitable. Binomial regression can be analyzed through the Generalized Linear Model (GLM) with a specifics link functions. Some of link functions usually used in binomial regressions are logit, probit, and complimentary log-log (cloglog). Both logit and probit are symmetrical links functions, while cloglog is asymmetrical. This study aims to compare all three link functions and evaluate them by employing the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) index. All three link function will be applied for binomial regression models using simulation and real data on school drop-out rates in East Java Indonesia. Based on the AIC and BIC index, it is evidenced that the cloglog gives the best performance than the logit and probit link function for the school drop-out rates model. Therefore, this case is considered appropriate using the asymmetrical assumptions.

Original languageEnglish
Title of host publication2nd International Conference on Science, Mathematics, Environment, and Education
EditorsNurma Yunita Indriyanti, Murni Ramli, Farida Nurhasanah
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735419452
DOIs
Publication statusPublished - 18 Dec 2019
Event2nd International Conference on Science, Mathematics, Environment, and Education, ICoSMEE 2019 - Surakarta, Indonesia
Duration: 26 Jul 201928 Jul 2019

Publication series

NameAIP Conference Proceedings
Volume2194
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2nd International Conference on Science, Mathematics, Environment, and Education, ICoSMEE 2019
Country/TerritoryIndonesia
CitySurakarta
Period26/07/1928/07/19

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