We discuss a general likelihood formula for the estimation of parameters in diffusion processes. The construction of the likelihood is based on the structure exposed by Liptser and Shiryayev. We apply the formula to some linear and non linear models. We consider the possibility of multivariate extensions of the theorem
We propose a simple, general and computationally efficient algorithm for maximum likelihood estima- ...
This thesis consists of five papers (Paper A-E) on statistical modeling of diffusion processes. Two ...
This thesis considers the problem of likelihood- based parameter estimation for time-homogeneous jum...
We present an approximate Maximum Likelihood estimator for univariate Ito stochastic differential eq...
Abstract. In this paper we consider a new model of multivariate lognormal diffusion pro-cess with a ...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
Certain aspects of maximum likelihood estimation for ergodic diffusions are studied via recently dev...
Certain aspects of maximum likelihood estimation for ergodic diffusions are studied via recently dev...
AbstractThe maximum likelihood estimation of the unknown parameter of a diffusion process based on a...
In this work a family of stochastic differential equations whose solutions are multidimensional diff...
In this work a family of stochastic differential equations whose solutions are multidimensional diff...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
The maximum likelihood estimation of the unknown parameter of a diffusion process based on an approx...
The purpose of this paper is to study some statistical problems: parameter estimation, binary detect...
We propose a simple, general and computationally efficient algorithm for maximum likelihood estima- ...
This thesis consists of five papers (Paper A-E) on statistical modeling of diffusion processes. Two ...
This thesis considers the problem of likelihood- based parameter estimation for time-homogeneous jum...
We present an approximate Maximum Likelihood estimator for univariate Ito stochastic differential eq...
Abstract. In this paper we consider a new model of multivariate lognormal diffusion pro-cess with a ...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
Certain aspects of maximum likelihood estimation for ergodic diffusions are studied via recently dev...
Certain aspects of maximum likelihood estimation for ergodic diffusions are studied via recently dev...
AbstractThe maximum likelihood estimation of the unknown parameter of a diffusion process based on a...
In this work a family of stochastic differential equations whose solutions are multidimensional diff...
In this work a family of stochastic differential equations whose solutions are multidimensional diff...
The transition density of a diffusion process does not admit an explicit expression in general, whic...
The maximum likelihood estimation of the unknown parameter of a diffusion process based on an approx...
The purpose of this paper is to study some statistical problems: parameter estimation, binary detect...
We propose a simple, general and computationally efficient algorithm for maximum likelihood estima- ...
This thesis consists of five papers (Paper A-E) on statistical modeling of diffusion processes. Two ...
This thesis considers the problem of likelihood- based parameter estimation for time-homogeneous jum...
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