Titlebook: Stable Processes and Related Topics; A Selection of Paper Stamatis Cambanis,Gennady Samorodnitsky,Murad S. T Book 1991 Birkhäuser Boston 19

Pantry

Bounded Stationary Stable Processes and Entropy,egral. The result is an application of Talagrand’s work on majorizing measures for stable processes [7]. We combine this result with earlier results to give necessary conditions for a stationary increment stable process to have a.s. bounded or a.s. continuous sample paths.

十字架

代理人

instructive

革新

An Extremal Problem in H, of the Upper Half Plane with Application to Prediction of Stochastic Procan explicit formula for the best approximation ... of ... in ... Using this formula for ..., we derive a formula for the best linear predictor of a continuous parameter “..-representable” complex regular stochastic process. The class of processes to which this formula applies includes the processes

hermitage

On Multiple Markov S,S Processes,2]) that a Gaussian process is n—ple Markov if and only if it has the so called Goursat representation introduced in [11]. In [11], a conjecture of Lévy [9], [10] is solved (Theorem 3.12). This says that the solution of the nth order stochastic differential equation with white noise input is n—ple M

Interim

思想上升

A Stochastic Integral Representation for the Bootstrap of the Sample Mean,quence of independent random variables distributed according to .. As Feller [3] shows, if 0 < . < 2, then there exist constants .. > 0 such that .(1 - .(...)) → .. as . → ∞, and for such .... = .. (..+...+ ..) converges in distribution to an .-stable random variable. If . = 2, then choose .. such t

不确定

Characterizations of ergodic stationary stable processes via the dynamical functional,X.:t∈ℝ} in the topology of convergence in measure and S. is the shift transformation. It is proved that a stochastically continuous process . is ergodic if and only if for each Y we have .This characterization is applied to symmetric stable processes to reprove and unify two independent equivalent c

Oratory

Capacities, Large Deviations and Loglog Laws,udied. Specific narrow large deviation principles and loglog laws are presented (without proof) for the Poisson process on the positive quadrant that is the natural foundation for extremal processes and spectrally positive stable motions. Related loglog laws for extremal processes and stable motions are discussed.