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, ...

In generalized linear models, the response is assumed to possess a probability distribution of the exponential form. That is, the probability density of the response Y for continuous response ...

GENMOD uses maximum likelihood estimation to fit generalized linear models. This family includes models for categorical data such as logistic, probit, and complementary log-log regression for binomial ...

The genes included those involved in the function of energy-producing mitochondria, lipid and iron metabolism, and the ...

2nd edition. Chapman & Hall. McCullagh, P. & Nelder, J.A. (1989) Generalized Linear Models. 2nd edition. Chapman & Hall. Agresti, A. (2015) Foundations of Linear and Generalized Linear Models. Wiley ...

and generalized linear and additive models. Emphasis is placed on analyzing real data using the R programming language. By completing this specialization, you will be able to: Correctly analyze and ...

Proper Generalized Decomposition (PGD) and model reduction techniques are essential ... invasive LATIN-PGD method for time-dependent non-linear problems in solid mechanics highlights the ...