The estimation of the noncentrality parameter of a chi-squared distribution, although simple to state, leads to difficulties, both for frequentist and Bayesian inferences. We propose in this paper a family of admissible improper Bayes estimators and study the minimax behavior of these estimators...

In this paper we compare via Monte Carlo experiments some methods to estimate the parameter of long-range dependence. We then apply these procedures to a financial time series to investigate its long-memory properties. The evidence of smooth long-range dependence makes the usual Efficiency...

Hodrick-Prescott (HP) Filter of (most often, seasonally adjusted) quaterly series is analysed. Some of the criticism to the filter are adressed. It is seen that, while filtering strongly affects autocorrelations, it has little effect on crosscorrelations. It is argued that the criticism that HP...

In this paper we show how the assumption that higher moments do not depend on the regressors can be exploited in a GMM framework, and we provide very simple estimators that are equivalent to GMM estimators. These simple estimators can be calculated by linear regressions which have been augmented...

The well-known Meixner class (Meixner, 1934) of probabilities on R has been recently extended to R^d (Pommeret, 1996). This generalized Meixner class corresponds to the simple quadratic natural exponential families charaterized by Casalis (1996). Following Lancaster (1975), we offer a...

This paper condiders an extension of Tran Van Hoa's family of 2SHI (two stage hierarchical information) estimators for the coefficient vector of a linear regression model and derives the conditions for the dominance of 2SHI estimator over the OLS and Stein rule estimators under a Generalised...

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...

In the setting of nonparametric hazard estimation under right random censorship by the kernel method, asynmptotic lower bounds for bandwidth selection are provided. If the error criterion is the integrated Squared Error (ISE), and if the distribution function of the underlying lifetime is...

We derive sharp asymptotic minimax bounds (that is, bounds which concern the exact asymptotic constant of the risk) for nonparametric density estimation based on weakly dependent observations. We study two particular problems for which there already exist such results in the case of independent...