Robust model selection using the out-of-bag bootstrap in linear regression.

ABSTRACT: Outlying observations have a large influence on the linear model selection process. In this article, we present a novel approach to robust model selection in linear regression to accommodate the situations where outliers are present in the data. The model selection criterion is based on two components, the robust conditional expected prediction loss, and a robust goodness-of-fit with a penalty term. We estimate the conditional expected prediction loss by using the out-of-bag stratified bootstrap approach. In the presence of outliers, the stratified bootstrap ensures that we obtain bootstrap samples that are similar to the original sample data. Furthermore, to control the undue effect of outliers, we use the robust MM-estimator and a bounded loss function in the proposed criterion. Specific

SUBMITTER: Rabbi F 

PROVIDER: S-EPMC9243146 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature