The purpose of statistical model selection is to identify a parsimonious model, which is a model that is as simple as possible while maintaining good predictive ability over the outcome of interest.
In linear regression with functional predictors and scalar responses, it may be advantageous, particularly if the function is thought to contain features at many scales, to restrict the coefficient ...
Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have ...
The least absolute shrinkage and selection operator ('lasso') has been widely used in regression shrinkage and selection. We extend its application to the regression model with autoregressive errors.
Risk Score Model of Aging-Related Genes for Bladder Cancer and Its Application in Clinical Prognosis
Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus were used to construct a 12-gene ARG-based prognostic signature through LASSO and Cox regression ...
TUESDAY, May 27, 2025 (HealthDay News) -- The least absolute shrinkage and selection operator-logistic regression (Lasso-LR) model is optimal for predicting in-hospital mortality for adult patients ...
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