Exploring elastic net and multivariate regression

TL;DR

This paper investigates the use of elastic net combined with multivariate regression to effectively reduce variables and analyze complex datasets with many predictors, demonstrating its practical utility.

Contribution

It introduces a novel approach of applying elastic net for variable reduction before multivariate regression on messy datasets, showcasing its effectiveness.

Findings

Elastic net successfully reduces variables in complex datasets.

The method improves interpretability of the data analysis.

It demonstrates the approach on a messy dataset, tidying it into meaningful subsets.

Abstract

When it comes to datasets with a tremendous amount of predictors, variable reduction techniques such as PCA or FA are often used. In this paper, the elastic net, which lies in between the LASSO method and ridge regression, is used as a variable reduction technique followed by further analysis with multivariate regression. Specifically, a messy only dataset is used to show how it can be 'tidied' up and broken down into sensible subsets using the aforementioned method.