Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology.

ABSTRACT: In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network analysis. However, several recognized limitations of lasso regularization may limit its suitability for psychological research. In this paper, we compare the properties of lasso approaches used for variable selection to Bayesian variable selection approaches. In particular we highlight advantages of stochastic search variable selection (SSVS), that make it well suited for variable selection applications in psychology. We demonstrate these advantages and contrast SSVS with lasso type penalization in an application to predict depre

SUBMITTER: Bainter SA 

PROVIDER: S-EPMC10202760 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature