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 ...

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 ...

Selecting a subset of variables for linear models remains an active area of research. This article reviews many of the recent contributions to the Bayesian model selection and shrinkage prior ...

Journal of Computational and Graphical Statistics, Vol. 22, No. 3, Special Issue: Advances in Markov Chain Monte Carlo (September 2013), pp. 729-748 (20 pages) This article describes methods for ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

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 ...

Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...

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 ...