@article{Bangchang2024, 
author = {Kannat Na Bangchang},
title = {Application of Bayesian variable selection in logistic regression model},
year = {2024},
journal = {AIMS Mathematics},
volume = {9},
number = {5},
pages = {13336-13345},
keywords = {Bayesian variable selection, multicollinearity problem, logistic regression model, LASSO method},
url = {https://www.sciopen.com/article/10.3934/math.2024650},
doi = {10.3934/math.2024650},
abstract = {Typically, in high dimensional data sets, many covariates are not significantly associated with a response. Moreover, those covariates are highly correlated, leading to a multicollinearity problem. Hence, the model is sparse since the coefficient of most covariates are likely to be zero. The classical frequentist or likelihood-based variable selection via any criterion such as Bayesian Information Criteria (BIC) and Akaike Information Criteria (AIC) or a stepwise subset selection becomes infeasible when the number of variables are large. An alternative solution is a Bayesian variable selection. In this study, we used a variable selection via a Bayesian variable selection and the least absolute shrinkage and selection operator (LASSO) method in the logistic regression model. Moreover, those methods were expanded to be applied to real datasets.}
}