The problem of parameter estimation for the non-stationary ergodic diffusion with Fisher-Snedecor invariant distribution, to be called Fisher-Snedecor diffusion, is considered. We propose generalized method of moments (GMM) estimator of unknown parameter, based on continuous-time observations, and prove its consistency and asymptotic normality. The explicit form of the asymptotic covariance matrix in asymptotic normality framework is calculated according to the new iterative technique based on evolutionary equations for the point-wise covariations. The results are illustrated in a simulation study covering various starting distributions and parameter values
We present a review of several results concerning invariant density estimation by observations of er...
Data available on continuous-time diffusions are always sampled discretely in time. In most cases, t...
Data available on continuos-time diffusions are always sampled discretely in time. In most cases, th...
The problem of parameter estimation for the non-stationary ergodic diffusion with Fisher-Snedecor in...
We consider the problem of parameter estimation for an ergodic diffusion with Fisher–Snedecor invari...
We consider the problem of testing the hypothesis on marginal distribution of ergodic diffusion with...
We consider the Fisher-Snedecor diffusion; that is, the Kolmogorov-Pearson diffusion with the Fisher...
This PhD thesis presents some new results on spectral properties and statistical analysis of ergodic...
In this thesis we consider theoretical and practical aspects of conducting inference on data coming ...
A number of discrete time, finite population size models in genetics describing the dynamics of alle...
AbstractWe consider adaptive maximum likelihood type estimation of both drift and diffusion coeffici...
prépublication SAMOS n°72National audienceWe propose an estimation method for the drift parameter of...
Parameter estimation problems of diffusion models are discussed. The problems of maximum likelihood ...
22 pagesThis paper deals with the problem of parameter estimation in the Cox-Ingersoll-Ross (CIR) mo...
We consider drift estimation problems for high dimension ergodic diffusion processes in nonparametri...
We present a review of several results concerning invariant density estimation by observations of er...
Data available on continuous-time diffusions are always sampled discretely in time. In most cases, t...
Data available on continuos-time diffusions are always sampled discretely in time. In most cases, th...
The problem of parameter estimation for the non-stationary ergodic diffusion with Fisher-Snedecor in...
We consider the problem of parameter estimation for an ergodic diffusion with Fisher–Snedecor invari...
We consider the problem of testing the hypothesis on marginal distribution of ergodic diffusion with...
We consider the Fisher-Snedecor diffusion; that is, the Kolmogorov-Pearson diffusion with the Fisher...
This PhD thesis presents some new results on spectral properties and statistical analysis of ergodic...
In this thesis we consider theoretical and practical aspects of conducting inference on data coming ...
A number of discrete time, finite population size models in genetics describing the dynamics of alle...
AbstractWe consider adaptive maximum likelihood type estimation of both drift and diffusion coeffici...
prépublication SAMOS n°72National audienceWe propose an estimation method for the drift parameter of...
Parameter estimation problems of diffusion models are discussed. The problems of maximum likelihood ...
22 pagesThis paper deals with the problem of parameter estimation in the Cox-Ingersoll-Ross (CIR) mo...
We consider drift estimation problems for high dimension ergodic diffusion processes in nonparametri...
We present a review of several results concerning invariant density estimation by observations of er...
Data available on continuous-time diffusions are always sampled discretely in time. In most cases, t...
Data available on continuos-time diffusions are always sampled discretely in time. In most cases, th...