Let X1,X2,...Xn be i.i.d. N-dimensional random variables having an unknown support of probability density denoted G; we suppose that G belongs to a functional class "g" of compact sets with smooth upper surface called boundary fragments. The problem consists in testing the hypotheses G=Go...

Expansions of Penalized Likelihood Ratio Statistics and Consequences on Matching Priors for HPD Regions.

The data consists of multivariate failure times under right random censorship. By the kernel smoothing technique, convolutions of cumulative multivariate hazard functions suggest estimators of the so- called multivariate hazard functions. We establish strong i.i.d. representations and uniform...

The paper deals with the problem of identifying stochastic unobserved two-component models, as in seasonal adjustment or trend-cycle decompositions. Solutions based on the properties of the unobserved component estimation error are considered, and analytical expressions for the variances and...

This paper discusses ways to reduce the bias of consistent estimators that are biased in finite samples. It is necessary only that the bias function, which relates parameter values to bias, should be estimable by computer simulation or by some other method. If so, bias can be reduced or even...

Many unit root and cointegration tests require an estimate of the spectral density function at frequency zero at some process. Kernel estimators based on weighted sums of autocovariances constructed using estimated residuals from an AR(1) regression are commonly used. However, it is known that...

The paper deals with the problem of identifying stochastic unobserved two-component models, as in seasonal adjustment or trend-cycle decompositions. Solutions based on the properties of the unobserved component estimation error are considered, and analytical expressions for the variances and...

This paper explains how the Gibbs sampler can be used to perform Bayesian inference on GARCH models. Although the Gibbs sampler is usually based on the analytical knowledge of the full conditional posterior densities, such knowledge is not available in regression models with GARCH errors. We...