La loi forte des grands nombres pour les processus harmonisables
Large and moderate deviations for moving average processes
Large deviations and strong mixing
Large deviations for generic stationary processes
Let (Ω,A,μ,T) be a measure preserving dynamical system. The speed of convergence in probability in the ergodic theorem for a generic function on Ω is arbitrarily slow.
Level crossings and other level functionals of stationary Gaussian processes.
Limit theorems for Banach-valued autoregressive processes. Applications to real continuous time processes.
Limit theory for some positive stationary processes with infinite mean
We prove stable limit theorems and one-sided laws of the iterated logarithm for a class of positive, mixing, stationary, stochastic processes which contains those obtained from nonintegrable observables over certain piecewise expanding maps. This is done by extending Darling–Kac theory to a suitable family of infinite measure preserving transformations.
Linear approximations to some non-linear AR(1) processes
Some methods for approximating non-linear AR(1) processes by classical linear AR(1) models are proposed. The quality of approximation is studied in special non-linear AR(1) models by means of comparisons of quality of extrapolation and interpolation in the original models and in their approximations. It is assumed that the white noise has either rectangular or exponential distribution.
Linear predictor for stationary processes in complete correlated actions
Linear rescaling of the stochastic process
Discussion on the limits in distribution of processes under joint rescaling of space and time is presented in this paper. The results due to Lamperti (1962), Weissman (1975), Hudson Mason (1982) and Laha Rohatgi (1982) are improved here.
Linear transformations of locally stationary processes
The paper deals with linear transformations of harmonizable locally stationary random processes. Necessary and sufficient conditions under which a linear transformation defines again a locally stationary process are given.
Loi de probabilité des maxima d'une fonction aléatoire stationnaire Somme d'une partie gaussienne et d'une partie sinusoïdale
Long memory time series models
Long time behaviour and stationary regime of memory gradient diffusions
In this paper, we are interested in a diffusion process based on a gradient descent. The process is non Markov and has a memory term which is built as a weighted average of the drift term all along the past of the trajectory. For this type of diffusion, we study the long time behaviour of the process in terms of the memory. We exhibit some conditions for the long-time stability of the dynamical system and then provide, when stable, some convergence properties of the occupation measures and of the...
Long-memory stable Ornstein-Uhlenbeck processes.