Robust Testing in Linear Models: The Infinitesimal Approach

In the presence of small deviations from the model (Figure 2), the χ 2 approximation of the classical test is extremely inaccurate (even for n = 100), its saddlepoint version and

Robust and Accurate Inference for Generalized Linear Models:..

By starting from a natural class of robust estimators for generalized linear models based on the notion of quasi-likelihood, we define robust deviances that can be used for

Generally,  in  psychology,  the  various  statistical  analyses  are  taught 

Sensitivity Analysis of the Predictive Distribution in (Generalized) Linear Models distribution based on the robust M -estimator (blue) gives a less distorted curve with respect to

By starting from a natural class of robust estimators for generalized linear models based on the notion of quasi-likelihood, we define robust deviances that can be used for

This is a key quantity in site occupancy models which typically remains unknown after the data are collected, because the probability of detecting a target species in a given quadrat

A two-step approach is employed to derive the conditional type-II maximum likelihood (ML-II) posterior distribution of the coefficients and individual effects.The ML-II