The purpose of statistical model selection is to identify a parsimonious model, which is a model that is as simple as possible while maintaining good predictive ability over the outcome of interest.

In today's data-driven world, statistical analysis plays a critical role in uncovering insights, validating hypotheses, and driving decision-making across industries. R, a powerful programming ...

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 ...

R is a powerful open source programming environment primarily known for its statistical capabilities. In this course we will cover some advanced applications of R: distributed computing using the ...

This article uses latent structure analysis to model ordered category ratings by multiple experts on the appropriateness of indications for the medical procedure carotid endarterectomy. The ...

This book, “Statistical Modeling and Computation,” provides a unique introduction to modern statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of ...

ABSTRACT A generalized method is presented to derive a ‘‘two scale’’ raindrop size distribution (DSD) model over a spatial or temporal domain in which a statistical rain parameter relation exists. The ...

A variety of statistical tools can detect potential breaches in the integrity of elections. These techniques draw on tools from many fields, including pure mathematics, statistics, and machine ...