Prediction Modeling With Many Correlated and Zero-Inflated Predictors: Assessing the Nonnegative Garrote Approach.

ABSTRACT: Building prediction models from mass-spectrometry data is challenging due to the abundance of correlated features with varying degrees of zero-inflation, leading to a common interest in reducing the features to a concise predictor set with good predictive performance given the experiments' resource-intensive nature. In this study, we established and examined regularized regression approaches designed to address zero-inflated and correlated predictors. In particular, we describe a novel two-stage regularized regression approach (ridge-garrote) explicitly modeling zero-inflated predictors using two component variables, comprising a ridge estimator in the first stage and subsequently applying a nonnegative garrotte estimator in the second stage. We contrasted ridge-garrote with one-stage meth

SUBMITTER: Gregorich M 

PROVIDER: S-EPMC12023833 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature