Jackknife estimator for anm-dependent stationary process

1992 ◽  
Vol 8 (2) ◽  
pp. 115-123 ◽  
Author(s):  
Jun Shao
Keyword(s):  
Stationary Process ◽  
Jackknife Estimator

The study of the process of evolution of the jet under outflow of water from the supercritical state through a thin nozzle

2016 ◽  
Vol 11 (1) ◽  
pp. 66-71 ◽  
Author(s):  
R.Kh. Bolotnova ◽  
V.A. Korobchinskaya
Keyword(s):  
Stationary Process ◽  
Supercritical State ◽  
System Of Differential Equations ◽  
Jet Formation ◽  
Cylindrical Coordinates ◽  
Initial Stage ◽  
Supersonic Regime ◽  
Water Outflow ◽  
Thin Nozzle ◽  
Supercritical Temperature

The dynamics of the water outflow from the initial supercritical state through a thin nozzle is studied. To describe the initial stage of non-stationary process outflow the system of differential equations of conservation of mass, momentum and energy in a two-dimensional cylindrical coordinates with axial symmetry is used. The spatial distribution of pressure and velocity of jet formation was received. It was established that a supersonic regime of outflow at supercritical temperature of 650 K is formed, which have a qualitative agreement for the velocity compared with the Bernoulli analytical solution and the experimental data.

Stability of stochastic differential equations driven by multifractional Brownian motion

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Oussama El Barrimi ◽  
Youssef Ouknine
Keyword(s):  
Brownian Motion ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Stationary Process ◽  
Strong Stability ◽  
Pathwise Uniqueness ◽  
Uniqueness Property ◽  
Selection Theorem ◽  
Multifractional Brownian Motion ◽  
Stability Results

Abstract Our aim in this paper is to establish some strong stability results for solutions of stochastic differential equations driven by a Riemann–Liouville multifractional Brownian motion. The latter is defined as a Gaussian non-stationary process with a Hurst parameter as a function of time. The results are obtained assuming that the pathwise uniqueness property holds and using Skorokhod’s selection theorem.

Properties and applications of stochastic processes with stationarynth-order increments

1974 ◽  
Vol 6 (3) ◽  
pp. 512-523 ◽  
Author(s):  
B. Picinbono
Keyword(s):  
Stochastic Processes ◽  
Stationary Process ◽  
General Description ◽  
Linear Filtering ◽  
Stationary Increments ◽  
Physical Problems

Many physical problems are described by stochastic processes with stationary increments. We present a general description of such processes. In particular we give an expression of a process in terms of its increments and we show that there are two classes of processes: diffusion and asymptotically stationary. Moreover, we show that thenth increments are given by a linear filtering of an arbitrary stationary process.

scholarly journals Extreme value theory for piecewise contracting maps with randomly applied stochastic perturbations

2016 ◽  
Vol 16 (03) ◽  
pp. 1660015 ◽  
Author(s):  
Davide Faranda ◽  
Jorge Milhazes Freitas ◽  
Pierre Guiraud ◽  
Sandro Vaienti
Keyword(s):  
Extreme Value Theory ◽  
Stationary Process ◽  
Higher Dimensions ◽  
Value Theory ◽  
Extreme Value ◽  
Extremal Index ◽  
Limiting Distributions ◽  
Stochastic Perturbations

We consider globally invertible and piecewise contracting maps in higher dimensions and perturb them with a particular kind of noise introduced by Lasota and Mackey. We got random transformations which are given by a stationary process: in this framework we develop an extreme value theory for a few classes of observables and we show how to get the (usual) limiting distributions together with an extremal index depending on the strength of the noise.

On estimation of the integrals of certain functions of spectral density

1980 ◽  
Vol 17 (01) ◽  
pp. 73-83 ◽  
Author(s):  
Masanobu Taniguchi
Keyword(s):  
Continuous Function ◽  
Spectral Density ◽  
Stationary Process ◽  
Asymptotic Variance ◽  
Asymptotic Properties ◽  
Gaussian Stationary Process

Let g(x) be the spectral density of a Gaussian stationary process. Then, for each continuous function ψ (x) we shall give an estimator of whose asymptotic variance is O(n –1), where Φ(·) is an appropriate known function. Also we shall investigate the asymptotic properties of its estimator.

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