The purpose of this paper is to investigate kernel density estimators for spatial processes with linear or nonlinear structures. Sufficient conditions for such estimators to converge in L1 are obtained under extremely general, verifiable conditions. The results hold for mixing as well as for...

Semiparametric proportional hazard regression models are the cornerstone in modern survival analysis. Most estimation methodologies developed in the literature, such as the famous partial likelihood based estimation, are built on the ground that the censoring is noninformative. However, in many...

It is shown that the representation theory of a multivariate, purely nondeterministic, wide sense stationary generalized process can be reduced to a study of some isomorphism results established for commutation relations occurring in quantum mechanics. Using this simplification a multiplicity...

Motivated by problems occurring in the empirical identification and modelling of a n-dimensional ARMA time series X(t) we study the possibility of obtaining a factorization (I + a1B + ... + apBp) X(t) = [[Pi]i=1p (I - [alpha]iB)] X(t), where B is the backward shift operator. Using a result in...

We consider a log-linear model for time series of counts. This type of model provides a framework where both negative and positive association can be taken into account. In addition time dependent covariates are accommodated in a straightforward way. We study its probabilistic properties and...

In this paper, we consider testing for additivity in a class of nonparametric stochastic regression models. Two test statistics are constructed and their asymptotic distributions are established. We also conduct a small sample study for one of the test statistics through a simulated example.