The reaction of EU bond and equity market volatilities to sovereign rating announcements (Standard & Poor’s, Moody’s, and Fitch) is investigated using a panel of daily stock market and sovereign bond returns. The parametric volatilities are defined using EGARCH specifications. The estimation...

For censored data, it is very common for the tail of the survival function to be non-identifiable because of the abundance of censored observations in the tail. This is especially prominent in censored regression quantile analysis, and introduces a serious problem with inference, especially near...

In this paper, we consider the problem of detecting edges in a Gaussian graphical model. The problem is equivalent to the identification of non-zero entries of the concentration matrix of a normally distributed random vector. Following the methodology initiated in Meinshausen and Bühlmann...

An application of nonparametric predictive inference for multinomial data (NPI) to classification tasks is presented. This model is applied to an established procedure for building classification trees using imprecise probabilities and uncertainty measures, thus far used only with the imprecise...

The basic structure of algorithms for numerical computation of optimal approximate linear regression designs is briefly summarized. First order methods are contrasted to second order methods. A first order method, also called a vertex direction method, uses a local linear approximation of the...

The problem of clustering probability density functions is emerging in different scientific domains. The methods proposed for clustering probability density functions are mainly focused on univariate settings and are based on heuristic clustering solutions. New aspects of the problem associated...

A supersaturated design (SSD) is a design whose run size is not enough for estimating all the main effects. An important goal in the analysis of such designs is to identify active effects based on the effect sparsity assumption. A Bayesian variable selection strategy which combines the...

Minimization of the L∞ norm, which can be viewed as approximately solving the non-convex least median estimation problem, is a powerful method for outlier removal and hence robust regression. However, current techniques for solving the problem at the heart of L∞ norm minimization are slow,...

Phase I clinical trials are conducted in order to find the maximum tolerated dose of a given drug out of a set of doses, usually finite. In general, once a formal target function and a suitable probability structure are defined, optimization of sequential studies can theoretically be achieved...

It is common knowledge that the performance of different learning algorithms depends on certain characteristics of the data—such as dimensionality, linear separability or sample size. However, formally investigating this relationship in an objective and reproducible way is not trivial. A new...