Linear mixed model (LMM) methodology is a powerful technology to analyze models containing both the fixed and random effects. The model was first proposed to estimate genetic parameters for ...

A generalized linear model extends the traditional linear model and is, therefore, applicable to a wider range of data analysis problems. A generalized linear model consists of the following ...

Generalized linear models are widely used by data analysts. However, the choice of the link function is often made arbitrarily. Here we permit the data to estimate the link function by incorporating ...

Bin Guo, Song Xi Chen, Tests for high dimensional generalized linear models, Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 78, No. 5 (NOVEMBER 2016), pp. 1079-1102 ...

Generalized Linear Models Generalized Linear Models Course Topics Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, ...

You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

Topics include: general theory of regression and generalised linear models, linear regression, logistic regression for binary data, models for ordered and unordered (nominal) responses, log-linear ...

Multiple regression and regression diagnostics. Generalised linear models; the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, deviance residuals.