A new edition of this popular text on robust statistics, thoroughly updated to include new and
improved methods and focus on implementation of methodology using the increasingly …

The task of statistical model selection is to choose a family of distributions among a possible
set of families, which is the best approximation of reality manifested in the observed data. In …

We propose a new approach to the selection of regression models based on combining a
robust penalized criterion and a robust conditional expected prediction loss function that is …

Robust model selection procedures control the undue influence that outliers can have on the
selection criteria by using both robust point estimators and a bounded loss function when …

Variable selection in the presence of outliers may be performed by using a robust version of
Akaike's information criterion (AIC). In this paper, explicit expressions are obtained for such …

This paper studies the problem of estimating the number of clusters in the context of logistic
regression clustering. The classification likelihood approach is employed to tackle this …

This paper addresses the problem of estimating the expressions or concentrations of
proteins from measurements obtained from protein arrays and illustrates the methodology …

We present the asymptotic distribution theory for M-estimators in non-linear regression
model with long-range dependence (LRD) for a general class of covariance functions in …

The forward search provides data-driven flexible trimming of a Cp statistic for the choice of
regression models that reveals the effect of outliers on model selection. An informed robust …

Selection of relevant predictor variables for building a model is an important problem in the
multiple linear regression. Variable selection method based on ordinary least squares …