options are provided: the conventional least absolute shrinkage and selection operator (LASSO) for metric covariates, and both … group and fused LASSO for categorical predictors. The methods are investigated both for simulated data and for two real data …

valuations. In this paper, we use the Lasso method which is based on a multivariate model and can identify patterns in the data … versions of the Lasso that apply different sub-penalties for individual covariates. In contrast to random forests or neural …

essential for the intended tasks such as classification, clustering or regression analysis. In gene expression microarray data …. Microarray data has typically several thousands of genes (features) but only tens of samples. Problems which can occur due to the … microarray data in order to select the most predictive genes. We compare classical approaches based on statistical tests with a …

inducing low portfolio turnover. Moreover, we find that simply using LASSO is insufficient to lower turnover when the model …

the Least Absolute Shortage and Shrinkage Operator (LASSO). Results were further confirmed through other machine …

regularization techniques- the Standard lasso, the Adaptive lasso, the Minimum Schwarz Bayesian information criterion lasso, and the … lasso inferential techniques of double-selection linear regression, partialing-out lasso linear regression, and partialing …-out lasso instrumental variable regression. Policy recommendations are also provided in line with the AfCFTA and the green …