Nous décrivons les états stationnaires invariants par translation (TIS) du processus d’exclusion asymétrique facilité unidimensionnel en temps continu, dans lequel une particule sur le site i∈Z saute vers i+1 (respectivement i−1) avec un taux p (resp. 1−p), à condition que i−1 i+1) soit occupé et vide. Tous TIS densité ρ≤1/2 sont supportés des configurations piégées lesquelles aucun deux sites ...

1 Preliminaries Let fX t g be a discrete-time stochastic process. Throughout, we will assume that any process fX t g is indexed by the integers or some subset of the integers (for example, the positive integers). In practice, we observe a nite length sample (or time series) X 1 ; ; X n from fX t g. (In this presentation, we will distinguish between a stochastic process fX t g and a nite time se...

The RER which is theoretically influenced by the real interest rate differential (RRE) and currency excess return (CER), is statistically examined during 1990-2016. Accordingly, the stationarity of RER as null hypothesis is not approved in the Iranian economy. Therefore, the TVAR method is examined to analyze the nonstationary RER sample to two sub-periods stationary process which are both stat...

Let {Xn} be a stationary and ergodic time series taking values from a finite or countably infinite set X . Assume that the distribution of the process is otherwise unknown. We propose a sequence of stopping times λn along which we will be able to estimate the conditional probability P (Xλn+1 = x|X0, . . . , Xλn) from data segment (X0, . . . , Xλn) in a pointwise consistent way for a restricted ...

We prove the equivalence among symmetricity, time reversibility, and zero entropy production of the stationary solutions of linear stochastic differential equations. A sufficient and necessary reversibility condition expressed in terms of the coefficients of the equations is given. The existence of a linear stationary irreversible process is established. Concerning reversibility, we show that t...

In this contribution we present a method for modeling a non-stationary process by a combination of fast learning and slowly learning modules, where the fast learning modules transform the input and output data for stable kernel module, which models a situation normalized to be stationary. The proposed method is applied in mod-eling a non-stationary chemical process.