In the Thesis the problem of estimating an unknown parameter in a stochastic dif- ferential equation is studied. Linear equations with Volterra process as the source of noise are considered. Firstly, the properties of Volterra processes and the properties of stochastic integral with respect to a Volterra process are presented. Secondly, the prop- erties of the solution to the equation under consideration are discussed. This includes the existence of the strictly stationary solution, the properties of such solution and ergodic results. These results are then generalized to equations with a mixed noise. Ergodic results are used to derive strongly consistent estimators of the unknown parameter.
In this paper we consider the problem of estimating parameters in ordinary differential equations gi...
The probabilistic distribution of local time of a homogeneous transient dif- fusion process is foun...
This article gives a short review of key issues and of existing estimation methods in differen-tial ...
Parameter estimation in stochastic differential equations and stochastic partial differential equati...
Abstract In this paper, we study the properties of continuity and differentiability of solutions to ...
Title: Stochastic Differential Equations with Gaussian Noise Author: Josef Janák Department: Departm...
Title: Stochastic Differential Equations with Gaussian Noise Author: Josef Janák Department: Departm...
Maximum-likelihood estimates of the parameters of stochastic differential equations are consistent a...
This paper considers the estimation of the parameters of general systems of stochastic differential-...
We investigate stochastic Volterra equations and their limiting laws. The stochastic Volterra equati...
2012-08-03While consistency of the maximum likelihood estimator of the unknown parameters in the sec...
Let (X(t), t ≥ −1) and (Y (t), t ≥ 0) be stochastic processes satisfying dX(t) = aX(t)dt+ bX(t − 1)...
AbstractWe consider Volterra type processes which are Gaussian processes admitting representation as...
UnrestrictedIn this work we discuss two problems related to stochastic partial differential equation...
Let (X(t), t ≥ −1) and (Y (t), t ≥ 0) be stochastic processes satisfying dX(t) = aX(t)dt + bX(t − 1)...
In this paper we consider the problem of estimating parameters in ordinary differential equations gi...
The probabilistic distribution of local time of a homogeneous transient dif- fusion process is foun...
This article gives a short review of key issues and of existing estimation methods in differen-tial ...
Parameter estimation in stochastic differential equations and stochastic partial differential equati...
Abstract In this paper, we study the properties of continuity and differentiability of solutions to ...
Title: Stochastic Differential Equations with Gaussian Noise Author: Josef Janák Department: Departm...
Title: Stochastic Differential Equations with Gaussian Noise Author: Josef Janák Department: Departm...
Maximum-likelihood estimates of the parameters of stochastic differential equations are consistent a...
This paper considers the estimation of the parameters of general systems of stochastic differential-...
We investigate stochastic Volterra equations and their limiting laws. The stochastic Volterra equati...
2012-08-03While consistency of the maximum likelihood estimator of the unknown parameters in the sec...
Let (X(t), t ≥ −1) and (Y (t), t ≥ 0) be stochastic processes satisfying dX(t) = aX(t)dt+ bX(t − 1)...
AbstractWe consider Volterra type processes which are Gaussian processes admitting representation as...
UnrestrictedIn this work we discuss two problems related to stochastic partial differential equation...
Let (X(t), t ≥ −1) and (Y (t), t ≥ 0) be stochastic processes satisfying dX(t) = aX(t)dt + bX(t − 1)...
In this paper we consider the problem of estimating parameters in ordinary differential equations gi...
The probabilistic distribution of local time of a homogeneous transient dif- fusion process is foun...
This article gives a short review of key issues and of existing estimation methods in differen-tial ...