Nature

Statistical model choice including variable selection based on variable importance: A relevant way for biomarkers selection to predict meat tenderness

In this paper, three regression models (parametric, semiparametric and nonparametric) are considered and estimated respectively by multiple linear regression (MLR), sliced inverse regression (SIR) and ...
Nature

Variable selection for inferential models with relatively high-dimensional data: Between method heterogeneity and covariate stability as adjuncts to robust selection

Variable selection in inferential modelling is problematic when the number of variables is large relative to the number of data points, especially when multicollinearity is present. A variety of ...
techtimes

The Role of Assumptions in Statistical Model Selection

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...
InfoWorld

Machine learning: Demystifying linear regression and feature selection

Businesspeople need to demand more from machine learning so they can connect data scientists’ work to relevant action. This requires basic machine learning literacy — what kinds of problems can ...
Investopedia

Linear Regression Excel: Step-by-Step Instructions

Troy Segal is an editor and writer. She has 20+ years of experience covering personal finance, wealth management, and business news. Eric's career includes extensive work in both public and corporate ...
Investopedia

Nonlinear vs. Linear Regression: Differences and Applications

Will Kenton is an expert on the economy and investing laws and regulations. He previously held senior editorial roles at Investopedia and Kapitall Wire and holds a MA in Economics from The New School ...