This paper develops an asymptotic theory for estimated change-points in linear and nonlinear time series models. Based on a measurable objective function, it is shown that the estimated change-point ...

The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance.

Hoerl and Kennard introduced a class of biased estimators (ridge estimators) for the parameters in an ill-conditioned linear model. In this paper the ridge estimators are viewed as a subclass of the ...

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, ...

Course Topics Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous ...

This unit builds on the basic ideas of linear models introduced in Statistics 1 (MATH 11400) and Linear Models (MATH 35110), and extends them to deal with more general specifications.

A Temple University environmental engineer has outlined new mathematical procedures, or techniques, to produce analytical solutions of the complex, non-linear equations of water flow in soils ...

To study both theoretical and practical aspects of statistical modeling, to develop the expertise in selecting and evaluating the model and interpreting the results. General Description of the Unit ...

In a new paper, researchers show that even the most sophisticated general-purpose AI language models struggle to solve math problems.