This paper studies the asymptotic and nite-sample performance ofpenalized regression methods when different selectors of theregularization parameter are used under the assumption that the truemodel is, or is not, included among the candidate model. In the lattersetting, we relax assumptions in...

The least squares linear regression estimator is well-known to be highly sensitive tounusual observations in the data, and as a result many more robust estimators havebeen proposed as alternatives. One of the earliest proposals was least-sum of absolutedeviations (LAD) regression, where the...

This paper discusses a novel application of mathematical programming techniques to a regression problem. While least squares regression techniques have been used fora long time, it is known that their robustness properties are not desirable. Specifically, the estimators are known to be too...

In this paper we investigate the effect of presmoothing on model selection. ChristobalChristobal et al. (1987) showed the beneficial effect of presmoothing for estimating the parameters in a linear regression model. Here, in a regression setting, we show that smoothing the response data prior to...

It is not unusual for the response variable in a regression model to be subject to censoring or truncation. Tobit regression models are a specific example of such a situation, where for some observations the observed response is not the actual response, but rather the censoring value...

An improved AIC-based criterion is derived for model selection in general smoothing-basedmodeling, including semiparametric models and additive models. Examples areprovided of applications to goodness-of-fit, smoothing parameter and variable selectionin an additive model and semiparametric...

Tree induction and logistic regression are two standard, off-the-shelf methodsfor building models for classification. We present a large-scale experimentalcomparison of logistic regression and tree induction, assessing classification accuracyand the quality of rankings based on class-membership...

This paper presents a new version of the RE-EM regression tree method for longitudinal and clustered data. The RE-EM tree is a methodology that combines the structure of mixed effects models for longitudinal and clustered data with the flexibility of tree-based estimation methods. The RE-EM tree...