Robust Inference for Generalized Linear Models

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...

Robust Inference in Generalized Linear Models

The weighted likelihood approach is used to perform robust inference on the parameters in a generalized linear models. We distinguish the case of replicated observations of the dependent variable for each combination of the explanatory variables, common in the design of experiment framework, and the case of one observation for each combination of the explanatory variables, very common in observ...

Robust inference in generalized partially linear models

In many situations, data follow a generalized partly linear model in which the mean of the responses is modeled, through a link function, linearly on some covariates and nonparametrically on the remaining ones. A new class of robust estimates for the smooth function η, associated to the nonparametric component, and for the parameter β, related to the linear one, is defined. The robust estimator...

Robust and Accurate Inference for Generalized Linear Models: Complete Computations

Robust Second Order Accurate Inference for Generalized Linear Models

Generalized linear models have become the most commonly used class of regression models in the analysis of a large variety of data. In particular, generalized linear model can be used to model the relationship between predictors and a function of the mean of a continuous or discrete response variable. The estimation of the parameters of the model can be carried out by maximum likelihood or quas...