In the paper we prove rates of strong convergence of M-estimators for the parameters in a general nonlinear autoregressive model. In the proofs we utilize a variational principle from stochastic optimization theory which was proved by Shapiro (Ann. Oper. Res. 30 (1991) 169). The application of...

The problem of optimal prediction in the stochastic linear regression model with infinitely many parameters is considered. We suggest a prediction method that outperforms asymptotically the ordinary least squares predictor. Moreover, if the random errors are Gaussian, the method is...

Classification of mixed categorical and continuous data is often performed using the location linear discriminant function which assumes across-location homoscedasticity. In this paper, we investigate the hazard arising from a routine application of the classifier under across-location...

In this article, multivariate density expansions for the sample correlation matrix R are derived. The density of R is expressed through multivariate normal and through Wishart distributions. Also, an asymptotic expansion of the characteristic function of R is derived and the main terms of the...

We provide in this paper asymptotic theory for the multivariate GARCH(p,q) process. Strong consistency of the quasi-maximum likelihood estimator (MLE) is established by appealing to conditions given by Jeantheau (Econometric Theory 14 (1998), 70) in conjunction with a result given by Boussama...

In this paper we investigate the tail behaviour of a random variable S which may be viewed as a functional T of a zero mean Gaussian process X, taking special interest in the situation where X obeys the structure which is typical for limiting processes occurring in nonparametric testing of...

In this paper we consider a model for dependent censoring and derive a consistent asymptotically normal estimator for the underlying survival distribution from a sample of censored data. The methodology is illustrated with an application to the analysis of cancer data. Some simulations to...

Given two linear regression models y1=X1[beta]1+u1 and y2=X2[beta]2+u2 where the response vectors y1 and y2 are unobservable but the sum y=y1+y2 is observable, we study the problem of decomposing y into components and , intended to be close to y1 and y2, respectively. We develop a theory of best...

Stationary covariance functions that model space-time interactions are in great demand. The goal of this paper is to introduce and develop new spatio-temporal stationary covariance models. Integral representations for covariance functions with certain properties, such as [alpha]-symmetry in the...

Using a recent result about the invariance problem in linear canonical analysis (LCA), we introduce a criterion by means of which one can see if this invariance holds when the related random vectors are transformed by linear maps. Then, the estimation of this criterion is considered as well as...