Many contemporary classifiers are constructed to provide good performance for very high dimensional data. However, an issue that is at least as important as good classification is determining which of the many potential variables provide key information for good decisions. Responding to this...

For high-dimensional data, most feature-selection methods, such as SIS and the lasso, involve ranking and selecting features individually. These methods do not require many computational resources, but they ignore feature interactions. A simple recursive approach, which, without requiring many...

In standard parametric classifiers, or classifiers based on nonparametric methods but where there is an opportunity for estimating population densities, the prior probabilities of the respective populations play a key role. However, those probabilities are largely ignored in the construction of...

In this paper we propose simple, general tiered classifiers for relatively complex data. Empirical studies on real and simulated data show that three two-tier classifiers, which are respective extensions of linear discriminant analysis, linear logistic regression and support vector machines, can...

We describe a unified approach to the construction of confidence bands in nonparametric density estimation and regression. Our techniques are based on interpolation formulae in numerical differentiation, and our arguments generate a variety of bands depending on the assumptions one is prepared...

It is shown that the zeros of the derivatives of the Cauchy density occur at quantiles that are regularly spaced in terms of probability. A few non-statistical consequences are obtained and the derivatives of the Student-t densities are give closed form expressions.

Variational methods have been proposed for obtaining deterministic lower bounds for log-likelihoods within missing data problems, but with little formal justification or investigation of the worth of the lower bound surfaces as tools for inference. We provide, within a general Markovian context,...