Robust Testing in Linear Models: The Infinitesimal Approach

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

Generally,  in  psychology,  the  various  statistical  analyses  are  taught 

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

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

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