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 stepwise …

When applying a statistical method in practice it often occurs that some observations deviate
from the usual assumptions. However, many classical methods are sensitive to outliers. The …

Robust statistics is an extension of classical statistics that specifically takes into account the
concept that the underlying models used to describe data are only approximate. Its basic …

Traffic crash datasets are often marred by the presence of anomalous data points, commonly
referred to as outliers. These outliers can have a profound impact on the results obtained …

The dynamic properties of many economic time series variables can be characterised as
state-dependent or regime-switching. A popular model to describe this type of non-linear …

Publisher Summary This chapter discusses positive-breakdown methods. It focusses on the
linear regression model. The goal of positive-breakdown methods is to be robust against the …

We are interested in testing hypotheses that concern the parameter of a logistic regression
model. A robust Wald-type test based on a weighted Bianco and Yohai [Bianco, AM, Yohai …

We propose robust tests as alternatives to the classical Wilks' Lambda test in one-way
MANOVA. The robust tests use highly robust and efficient multisample multivariate S …

Weighted versions of the likelihood ratio, Wald, score and disparity tests are proposed for
parametric inference. If the parametric model is correct, the weighted likelihood tests are …

Publisher Summary This chapter discusses the concept of robust inference using an
approach known as influence functions. It also describes the two key tools, the influence …