Analysis of multisite intervention studies using generalized linear mixed models

Analysis of Recognition Algorithms using Linear, Generalized Linear, and Generalized Linear Mixed Models

This paper discusses the design and use of linear, generalized linear, and generalized linear mixed models for evaluation and comparison of human face recognition algorithms. These models are introduced in a cohesive framework, and their benefits (compared to simple one-way descriptive comparisons) are reviewed. Several example analyses involving algorithm configuration and subject covariates i...

Assessing Generalized Linear Mixed Models Using Residual Analysis

A nonparametric smoothing method for assessing the adequacy of generalized linear mixed models (GLMMs) is developed. The proposed method is based on smoothing the residuals over continuous covariates to avoid the partition of continuous covariates on model checking. The global test statistic has a quadratic form and its formulae of expectation as well as variance are derived. The sampling distr...

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

Generalized Linear Mixed Models

Generalized linear models (GLMs) represent a class of fixed effects regression models for several types of dependent variables (i.e., continuous, dichotomous, counts). McCullagh and Nelder [32] describe these in great detail and indicate that the term ‘generalized linear model’ is due to Nelder and Wedderburn [35] who described how a collection of seemingly disparate statistical techniques coul...

Generalized Linear Mixed Models