This paper proposes a novel computationally efficient method that integrates data imputation and variable selection to address these issues.

Abstract · Introduction · Methods · Simulation study

Regression analysis is often impacted by several challenges such as high dimensionality, severe multicollinearity, and missing data.

We propose an algorithm that employs the horseshoe shrinkage prior for shrinkage and multiple imputation for missing data in high-dimensional settings.

Regression analysis is often affected by high dimensionality, severe multicollinearity, and a large proportion of missing data.

We propose a Multiple Imputation Random Lasso (MIRL) method to select important variables and to predict the outcome for an epidemiologi-.

Mar 25, 2016 · MIRL outperforms other methods in high-dimensional scenarios in terms of both reduced prediction error and improved variable selection ...

Modern Statistics has entered the era of Big Data, wherein data sets are too large, high-dimensional, incomplete and complex for most classical statistical ...

Aug 21, 2018 · A new variable selection method is proposed. It extends classical variable selection methods in the case of high-dimensional data with or without missing data.

The goal of this study is to propose approaches to select important variables from incomplete high-dimensional data.