This unit builds on the basic ideas of linear models introduced in Probability and Statistics 1 (MATH 11340), and Linear Models (MATH 35110), and extends them to deal with more general specifications.

We study parameter estimation in linear Gaussian covariance models, which are p-dimensional Gaussian models with linear constraints on the covariance matrix. Maximum likelihood estimation for this ...

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, ...

This article studies optimal model averaging for partially linear models with heteroscedasticity. A Mallows-type criterion is proposed to choose the weight. The resulting model averaging estimator is ...

Phi-4 and an rStar-Math paper suggest that compact, specialized models can provide powerful alternatives to the industry’s largest systems.

New study shows why simulated reasoning AI models don’t yet live up to their billing Top AI models excel at math problems but lack reasoning needed for Math Olympiad proofs.

AI models solved math problems by processing them using natural language AI could soon tackle unsolved research problems, says math professor and former champion OpenAI self-published results ...