Robust inference in generalized partially 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 Based on Quasi- likelihoods for Generalized Linear Models and Longitudinal Data

In this paper we introduce and develop robust versions of quasilikelihood functions for model selection via an analysis-of-deviance type of procedure in generalized linear models and longitudinal data analysis. These robust functions are built upon natural classes of robust estimators and can be seen as weighted versions of their classical counterparts. The asymptotic theory of these test stati...

Robust-BD Estimation and Inference for General Partially Linear Models

The classical quadratic loss for the partially linear model (PLM) and the likelihood function for the generalized PLM are not resistant to outliers. This inspires us to propose a class of “robust-Bregman divergence (BD)” estimators of both the parametric and nonparametric components in the general partially linear model (GPLM), which allows the distribution of the response variable to be partia...

Robust and Accurate Inference for Generalized Linear Models: Complete Computations

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...