Discrete sampling of an integrated diffusion process and parameter estimation of the diffusion coefficient

Arnaud Gloter

Displaying similar documents to “Discrete sampling of an integrated diffusion process and parameter estimation of the diffusion coefficient”

Countable systems of degenerate stochastic differential equations with applications to super-Markov chains.

Bass, Richard F., Perkins, Edwin A. (2004)

Electronic Journal of Probability [electronic only]

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$M$ -estimation in nonlinear regression for longitudinal data

Martina Orsáková (2007)

Kybernetika

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The longitudinal regression model $Z_{i}^{j} = m (θ_{0}, 𝕏_{i} (T_{i}^{j})) + ε_{i}^{j},$ where $Z_{i}^{j}$ is the $j$ th measurement of the $i$ th subject at random time $T_{i}^{j}$ , $m$ is the regression function, $𝕏_{i} (T_{i}^{j})$ is a predictable covariate process observed at time $T_{i}^{j}$ and $ε_{i}^{j}$ is a noise, is studied in marked point process framework. In this paper we introduce the assumptions which guarantee the consistency and asymptotic normality of smooth $M$ -estimator of unknown parameter $θ_{0}$ .

Dependent Lindeberg central limit theorem and some applications

Jean-Marc Bardet, Paul Doukhan, Gabriel Lang, Nicolas Ragache (2008)

ESAIM: Probability and Statistics

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In this paper, a very useful lemma (in two versions) is proved: it simplifies notably the essential step to establish a Lindeberg central limit theorem for dependent processes. Then, applying this lemma to weakly dependent processes introduced in Doukhan and Louhichi (1999), a new central limit theorem is obtained for sample mean or kernel density estimator. Moreover, by using the subsampling, extensions under weaker assumptions of these central limit theorems are provided. All the...

Interacting brownian particles and Gibbs fields on pathspaces

David Dereudre (2003)

ESAIM: Probability and Statistics

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In this paper, we prove that the laws of interacting brownian particles are characterized as Gibbs fields on pathspace associated to an explicit class of hamiltonian functionals. More generally, we show that a large class of Gibbs fields on pathspace corresponds to brownian diffusions. Some applications to time reversal in the stationary and non stationary case are presented.

Displaying similar documents to “Discrete sampling of an integrated diffusion process and parameter estimation of the diffusion coefficient”

Countable systems of degenerate stochastic differential equations with applications to super-Markov chains.

M -estimation in nonlinear regression for longitudinal data

Dependent Lindeberg central limit theorem and some applications

Interacting brownian particles and Gibbs fields on pathspaces

$M$ -estimation in nonlinear regression for longitudinal data