Application of Bayesian variable selection in logistic regression model
Abstrak
<abstract> <p>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.</p> </abstract>
Penulis (1)
Kannat Na Bangchang
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Sumber Database
- CrossRef
- DOI
- 10.3934/math.2024650
- Akses
- Terbatas