@article{article_814361, title={LASSO Estimator in Logistic Regression for Small Data Sets}, journal={Veri Bilimi}, volume={4}, pages={69–72}, year={2021}, author={Yaman, Aslı and Cengiz, Mehmet Ali}, keywords={LASSO, Bernoulli distribution, Logistic regression, Feature selection}, abstract={Variable selection is an important subject in regression analysis. In regression analysis, the LASSO (Least Absolute Shrinkage and Selection Operator) provides sparse solutions to lead to variable selection. LASSO is a useful tool to achieve the shrinkage and variable selection simultaneously and the LASSO penalty term can shrink the parameter estimates toward exactly to zero. It is used generally in large data sets but in this article, we consider the variable selection problem for the multivariate Bernoulli logistic models adopting some information criteria especially in small data sets. Results of simulation were compared according to the four different criteria used for model selection.}, number={1}, publisher={Murat GÖK}, organization={Ondokuz Mayıs Üniversitesi}