AbstractThe consistency and asymptotic linearity of recursive maximum likelihood estimator is proved under some regularity and ergodicity assumptions on the logarithmic derivative of a transition density for a general statistical model. ©1998 Elsevier Science B.V
This paper introduces a family of recursively defined estimators of the parameters of a diffusion pr...
This thesis is concerned with the theoretical and practical aspects of some problems in Bayesian tim...
International audienceWe consider $N$ independent stochastic processes $(X_i(t), t\in [0,T_i])$, $i=...
The consistency and asymptotic linearity of recursive maximum likelihood estimator is proved under s...
AbstractThe consistency and asymptotic linearity of recursive maximum likelihood estimator is proved...
AbstractMany generalizations of the Robbins-Monro process have been proposed for the purpose of recu...
AbstractRobust estimation of parameters may be obtained via stochastic approximation algorithms. Thi...
This thesis is primarily concerned with the investigation of asymptotic properties of the maximum l...
AbstractAsymptotically maximum likelihood estimators and estimators asymptotically minimizing criter...
AbstractLet {Xj: j ⩾ 1} be a real-valued stationary process. Recursive kernel estimators of the join...
AbstractRecursive parameter estimation in diffusion processes is considered. First, stability and as...
This paper focuses on the estimation of smoothing distributions in general state space models where ...
AbstractLet {Xj}j = − ∞∞ be a vector-valued stationary process with a first-order univariate probabi...
AbstractWe study the estimation problem for a continuous (Gaussian) process with independent increme...
AbstractLet {Xj} ∞j=−∞ be a real-valued stationary process. Recursive kernel estimators of the joint...
This paper introduces a family of recursively defined estimators of the parameters of a diffusion pr...
This thesis is concerned with the theoretical and practical aspects of some problems in Bayesian tim...
International audienceWe consider $N$ independent stochastic processes $(X_i(t), t\in [0,T_i])$, $i=...
The consistency and asymptotic linearity of recursive maximum likelihood estimator is proved under s...
AbstractThe consistency and asymptotic linearity of recursive maximum likelihood estimator is proved...
AbstractMany generalizations of the Robbins-Monro process have been proposed for the purpose of recu...
AbstractRobust estimation of parameters may be obtained via stochastic approximation algorithms. Thi...
This thesis is primarily concerned with the investigation of asymptotic properties of the maximum l...
AbstractAsymptotically maximum likelihood estimators and estimators asymptotically minimizing criter...
AbstractLet {Xj: j ⩾ 1} be a real-valued stationary process. Recursive kernel estimators of the join...
AbstractRecursive parameter estimation in diffusion processes is considered. First, stability and as...
This paper focuses on the estimation of smoothing distributions in general state space models where ...
AbstractLet {Xj}j = − ∞∞ be a vector-valued stationary process with a first-order univariate probabi...
AbstractWe study the estimation problem for a continuous (Gaussian) process with independent increme...
AbstractLet {Xj} ∞j=−∞ be a real-valued stationary process. Recursive kernel estimators of the joint...
This paper introduces a family of recursively defined estimators of the parameters of a diffusion pr...
This thesis is concerned with the theoretical and practical aspects of some problems in Bayesian tim...
International audienceWe consider $N$ independent stochastic processes $(X_i(t), t\in [0,T_i])$, $i=...
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