Five decades ago, Bhattacharyya established a series of lower bounds for the variance of an unbiased estimator, since then called the Bhattacharyya bounds. In 1974 Blight and Rao have shown that the series of Bhattacharyya bounds converges to the variance of the best unbiased estimator. In this...

The paper considers parametric and nonparametric estimation of the distribution function F. Issues of particular interest are the identification properties of this model and, in the nonparametric case, the speed of convergence of the estimator F. The latter depends upon the properties of the...

This paper proposes alternative methods for constructing estimators from accept-reject samples by incorporating the variables rejected by the algorithm.

The setup of our study is the Principal Component Analysis for dependent Hilbert space valued random vectors with examples taken from the ARH (1) model. We prove that convergence rates of the j ^(th) empirical random projection operator II from n to j for almost sure convergence and convergence...

The problem of approximating a general regression function m(x)= E(Y|X=x) is addressed. As in the case of the classical L2-type projection pursuit regression considered by Hall (1989), we propose to approximate m(x) through a regression of Y given an index, that is a unidimensional projection of X.

This paper synthesises a global approach to both Bayesian and likelihood treatments of the estimation of the parameters of a hidden Markov model for the cases of normal and Poisson underlying distribution.

The aim of this paper is to relate some recent results on Levy processes (see Schoutens and Teugels, 1998) to a recent study of the author (1996) on multidimensional natural exponential families. In this way, we consider a natural construction of Sheffer polynomials associated to a d-dimensional...

We present a simulation model designed to determine the impact on congestion of policies for dealing with non-recurrent congestion (i.e. travel time uncertainty).

This paper puts forth a concept of Adptivety Rational Equilibrium (A.R.E) where agents base decisions upon predictions of future values of endogenous variables whose actual values are determined by equilibrium equations.