Università della Svizzera italiana

Variable selection in additive models by nonnegative garrote

Cantoni, Eva ; Mills Flemming, Joanna ; Ronchetti, Elvezio

In: Statistical modelling, 2011, vol. 11, no. 3, p. 237-252

We adapt Breiman’s (1995) nonnegative garrote method to perform variable selection in nonparametric additive models. The technique avoids methods of testing for which no general reliable distributional theory is available. In addition it removes the need for a full search of all possible models, something which is computationally intensive, especially when the number of variables is...

Università della Svizzera italiana

Robust inference for generalized linear models

Cantoni, Eva ; Ronchetti, Elvezio

In: Journal of the American Statistical Association, 2001, vol. 96, no. 455, p. 1022-1030

By starting from a natural class of robust estimators for generalized linear models based on the notion of quasi-likelihood, we de¯ne robust deviances that can be used for stepwise model selection as in the classical framework. We derive the asymptotic distribution of tests based on robust deviances and we investigate the stability of their asymptotic level under contamination. The binomial and...

Università della Svizzera italiana

A robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures

Cantoni, Eva ; Ronchetti, Elvezio

In: Journal of Health Economics, 2006, vol. 25, no. 2, p. 198-213

In this paper robust statistical procedures are presented for the analysis of skewed and heavy-tailed outcomes as they typically occur in health care data. The new estimators and test statistics are extensions of classical maximum likelihood techniques for generalized linear models. In contrast to their classical counterparts, the new robust techniques show lower variability and excellent...

Università della Svizzera italiana

Variable selection for marginal longitudinal generalized linear models

Cantoni, Eva ; Mills-Flemming, Joanna ; Ronchetti, Elvezio

In: Biometrics, 2005, vol. 61, no. 2, p. 507-514

Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. In this paper we propose a generalized version of Mallows's Cp (GCp) suitable for use with both parametric and nonparametric models. GCp provides an estimate of a measure of model's adequacy for prediction. We examine its performance with popular...