The adjusted r-squared is helpful for multiple regression and corrects for erroneous regression, giving you a more accurate ...

We describe how to conduct a regression analysis for competing risks data. The use of an add-on package for the R statistical software is described, which allows for the estimation of the ...

Troy Segal is an editor and writer. She has 20+ years of experience covering personal finance, wealth management, and business news. Catherine Falls Commercial/Getty Images Linear regression is a type ...

GPR works well with small datasets and generates a metric of confidence of a predicted result, but it's moderately complex and the results are not easily interpretable, says Dr. James McCaffrey of ...

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on this powerful machine learning technique used to predict a single numeric value. A regression problem is one ...

Andriy Blokhin has 5+ years of professional experience in public accounting, personal investing, and as a senior auditor with Ernst & Young. Thomas J Catalano is a CFP and Registered Investment ...

We introduce a new sparse sliced inverse regression estimator called Cholesky matrix penalization, and its adaptive version, for achieving sparsity when estimating the dimensions of a central subspace ...

Matrix-covariate is now frequently encountered in many biomedical researches. It is common to fit conventional statistical models by vectorizing matrix-covariate. This strategy results in a large ...