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...

This paper studies the asymptotic and nite-sample performance of penalized regression methods when different selectors of the regularization parameter are used under the assumption that the true model is, or is not, included among the candidate model. In the latter setting, we relax assumptions...

Although the histogram is the most widely used density estimator, it is well--known that the appearance of a constructed histogram for a given bin width can change markedly for different choices of anchor position. In this paper we construct a stability index $G$ that assesses the potential...

There are many different missing data methods used by classification tree algorithms, but few studies have been done comparing their appropriateness and performance. This paper provides both analytic and Monte Carlo evidence regarding the effectiveness of six popular missing data methods for...

Nonparametric regression techniques provide an e ective way of identifying and examiningstructure in regression data The standard approaches to nonparametric regression suchas local polynomial and smoothing spline estimators are sensitive to unusual observations and alternatives designed to be...

Nonparametric regression techniques provide an effective way of identifying and examining structure in regression data. The standard approaches to nonparametric regression, such as local polynomial and smoothing splineestimators, are sensitive to unusual observations, and alternatives designedto...

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...