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, ...

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

Scandinavian Journal of Statistics, Vol. 43, No. 4 (December 2016), pp. 1035-1045 (11 pages) Linear structural equation models, which relate random variables via linear inter-dependencies and Gaussian ...

Linear mixed models (LMMs) are a powerful and established tool for studying genotype–phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a ...

This is a preview. Log in through your library . Abstract In some applications linear approximations to non-linear models are desirable. They bring the benefits of computational simplicity and access ...