In this article we give a necessary and su±cient condition for a selfnormalized weak invariance principle, in the case of a strictly stationary Á-mixing sequence fXjgj¸1. This is obtained under the assumptions that the function L(x) = EX2 1 1fjX1·xg is slowly varying at 1 and the mixing...

In this paper we study strong approximations (invariance principles) of the sequential uniform and general Bahadur-Kiefer processes of long-range dependent sequences. We also investigate the strong and weak asymptotic behavior of the sequential Vervaat process, i.e., the integrated sequential...

We obtain high-density fluctuation limits for trajectories of the motions in Cox systems of independent motions in "Rd". The motions are quite general; they include a large class of diffusions, Brownmian bridges and fractional Brownian motions. The limits take values ina space of distributions...

We constructs a class of seperprocesses by taking the high density limit of a sequence of interacting-branching particle systems. The spatial motion of the superprocess is determined by a system of interacting diffusions, the branching density is given by an arbitary bounded non-negative Borel...

We introduce adapted sets and optional sets and we study a type of strong Markov property for set-indexed precesses, that can be associated with the sharp Markov property defined by Ivanoff and Merzbach (2000a).

We propose a Poisson modelling approach to random effects Cox proportional hazards models. Specifically we describe methods of statistical inference for a class of random effects Cox models which accommodate a wide range of nested random effects distributions. The orthodox BLUP approach to...

We introduce the idea that resampling from past observations in a Markov Chain Monte Carlo sampler can fasten convergence. We prove that proper resampling from the past does not disturb the limit distribution of the algorithm. We illustrate the method with two examples. The first on a Bayesian...

In this paper we study high moment partial sum processes based on residuals of a stationary ARMA model with or without a unknown mean parameter. We show that they can be approximated in probability by the analogous processes which are obtained from the independent and identically distributed...

This paper proposes an adaptive version for the Metropolis adjusted Langevin algorithm with a truncated drift (T-MALA). The scale parameter and the covariance matrix of the proposal kernel of the algorithm are simultaneously and recursively updated in order to reach the optimal acceptance rate...

We study a long-range dependence Gaussian process which we call “sub-fractional Brownian motion” (sub-fBm), because it is intermediate between Brownian motion (Bm) and fractional Brownian motion (fBm) in the sense that it has properties analogous to those of fBm, but the increments on...