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A note on Markov operators and transition systems

Bartosz Frej (2002)

Colloquium Mathematicae

On a compact metric space X one defines a transition system to be a lower semicontinuous map X 2 X . It is known that every Markov operator on C(X) induces a transition system on X and that commuting of Markov operators implies commuting of the induced transition systems. We show that even in finite spaces a pair of commuting transition systems may not be induced by commuting Markov operators. The existence of trajectories for a pair of transition systems or Markov operators is also investigated.

A note on maximal estimates for stochastic convolutions

Mark Veraar, Lutz Weis (2011)

Czechoslovak Mathematical Journal

In stochastic partial differential equations it is important to have pathwise regularity properties of stochastic convolutions. In this note we present a new sufficient condition for the pathwise continuity of stochastic convolutions in Banach spaces.

A note on maximal inequality for stochastic convolutions

Erika Hausenblas, Jan Seidler (2001)

Czechoslovak Mathematical Journal

Using unitary dilations we give a very simple proof of the maximal inequality for a stochastic convolution 0 t S ( t - s ) ψ ( s ) d W ( s ) driven by a Wiener process W in a Hilbert space in the case when the semigroup S ( t ) is of contraction type.

A note on one-dimensional stochastic equations

Hans-Jürgen Engelbert (2001)

Czechoslovak Mathematical Journal

We consider the stochastic equation X t = x 0 + 0 t b ( u , X u ) d B u , t 0 , where B is a one-dimensional Brownian motion, x 0 is the initial value, and b [ 0 , ) × is a time-dependent diffusion coefficient. While the existence of solutions is well-studied for only measurable diffusion coefficients b , beyond the homogeneous case there is no general result on the uniqueness in law of the solution. The purpose of the present note is to give conditions on b ensuring the existence as well as the uniqueness in law of the solution.

A note on optimal probability lower bounds for centered random variables

Mark Veraar (2008)

Colloquium Mathematicae

We obtain lower bounds for ℙ(ξ ≥ 0) and ℙ(ξ > 0) under assumptions on the moments of a centered random variable ξ. The estimates obtained are shown to be optimal and improve results from the literature. They are then applied to obtain probability lower bounds for second order Rademacher chaos.

A note on Poisson approximation.

Paul Deheuvels (1985)

Trabajos de Estadística e Investigación Operativa

We obtain in this note evaluations of the total variation distance and of the Kolmogorov-Smirnov distance between the sum of n random variables with non identical Bernoulli distributions and a Poisson distribution. Some of our results precise bounds obtained by Le Cam, Serfling, Barbour and Hall.It is shown, among other results, that if p1 = P (X1=1), ..., pn = P (Xn=1) satisfy some appropriate conditions, such that p = 1/n Σipi → 0, np → ∞, np2 → 0, then the total variation distance between X1+...+Xn...

A note on Poisson approximation by w-functions

M. Majsnerowska (1998)

Applicationes Mathematicae

One more method of Poisson approximation is presented and illustrated with examples concerning binomial, negative binomial and hypergeometric distributions.

A note on Pólya's theorem.

Dinis Pestana (1984)

Trabajos de Estadística e Investigación Operativa

The class of extended Pólya functions Ω = {φ: φ is a continuous real valued real function, φ(-t) = φ(t) ≤ φ(0) ∈ [0,1], límt→∞ φ(t) = c ∈ [0,1] and φ(|t|) is convex} is a convex set. Its extreme points are identified, and using Choquet's theorem it is shown that φ ∈ Ω has an integral representation of the form φ(|t|) = ∫0∞ max{0, 1-|t|y} dG(y), where G is the distribution function of some random variable Y. As on the other hand max{0, 1-|t|y} is the characteristic function of an absolutely continuous...

A note on prediction for discrete time series

Gusztáv Morvai, Benjamin Weiss (2012)

Kybernetika

Let { X n } be a stationary and ergodic time series taking values from a finite or countably infinite set 𝒳 and that f ( X ) is a function of the process with finite second moment. Assume that the distribution of the process is otherwise unknown. We construct a sequence of stopping times λ n along which we will be able to estimate the conditional expectation E ( f ( X λ n + 1 ) | X 0 , , X λ n ) from the observations ( X 0 , , X λ n ) in a point wise consistent way for a restricted class of stationary and ergodic finite or countably infinite alphabet time series...

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