For a wide class of second-order stationary spatial processes on a lattice, the statistical properties of the maximum Gaussian pseudo-likelihood estimators are studied. The estimators are natural as they imitate the theoretical prototypes of spatial best linear prediction. Under certain...

The paper introduces a new nonparametric estimator of the spectral density that is given in smoothing the periodogram by the probability density of Beta random variable (Beta kernel). The estimator is proved to be bounded for short memory data, and diverges at the origin for long memory data....

This paper considers the problem of statistical inference in linear regression models whose stochastic regressors and errors may exhibit long-range dependence. A time-domain sieve-type generalized least squares (GLS) procedure is proposed based on an autoregressive approximation to the...

In this paper robustness properties are studied for kernel density estimators. A plug-in and least squares cross-validation bandwidth selector are considered. In an asymptotic analysis and in a simulation study it is shown that the robustness of kernel density estimates depends strongly on the...

In this paper, we show how to estimate consistently the degree of fractional integration at a given frequency K, for both stationary and non stationary long-memory process. The statistics used are the periodigram for values Kn which converge to K with an appropriate rate. We also introduce tests...

It is well-known that traditional inference do not apply when the spectral density of a stationary process vanishes for some frequency. This paper examines some properties of several new non parametric tests of this hypothesis which have been recently proposed by Lacroix (1999). These tests...