Continuous-discrete state space models, Stochastic differential equations, Itô calculus, Sampling, Kalman filtering, Approximate nonlinear filtering, Structural equations modeling, Spatial models, Random fields, Stochastic partial differential equations,
A transformation is introduced to effectively remove level effects, i.e. the state dependency of the...
The application of the continuous state space model to unequally spaced sequence data is discussed a...
In this paper, a method is introduced for approximating the likelihood for the unknown parameters of...
Stochastic differential equations (SDE) are used as dynamical models for cross sectional discrete ti...
This paper discusses the estimation of a class of nonlinear state space models including nonlinear p...
This unified treatment of linear and nonlinear filtering theory presents material previously availab...
Contains fulltext : 55811.pdf (publisher's version ) (Closed access)Although convi...
In this paper, we propose using an ensemble Kalman filter (EnKF) and particle filters (PFs) to obtai...
This article gives a short review of key issues and of existing estimation methods in differen-tial ...
In this thesis two probabilistic model-based estimators are introduced that allow the reconstruction...
In applied microeconometric panel data analyses, time-constant random ef-fects and first-order Marko...
State space model is a class of models where the observations are driven by underlying stochastic pr...
This paper deals with the state estimation of the nonlinear stochastic models with continuous dynami...
summary:The paper deals with a filter design for nonlinear continuous stochastic systems with discre...
In principle, general approaches to optimal nonlinear filtering can be described in a unified way fr...
A transformation is introduced to effectively remove level effects, i.e. the state dependency of the...
The application of the continuous state space model to unequally spaced sequence data is discussed a...
In this paper, a method is introduced for approximating the likelihood for the unknown parameters of...
Stochastic differential equations (SDE) are used as dynamical models for cross sectional discrete ti...
This paper discusses the estimation of a class of nonlinear state space models including nonlinear p...
This unified treatment of linear and nonlinear filtering theory presents material previously availab...
Contains fulltext : 55811.pdf (publisher's version ) (Closed access)Although convi...
In this paper, we propose using an ensemble Kalman filter (EnKF) and particle filters (PFs) to obtai...
This article gives a short review of key issues and of existing estimation methods in differen-tial ...
In this thesis two probabilistic model-based estimators are introduced that allow the reconstruction...
In applied microeconometric panel data analyses, time-constant random ef-fects and first-order Marko...
State space model is a class of models where the observations are driven by underlying stochastic pr...
This paper deals with the state estimation of the nonlinear stochastic models with continuous dynami...
summary:The paper deals with a filter design for nonlinear continuous stochastic systems with discre...
In principle, general approaches to optimal nonlinear filtering can be described in a unified way fr...
A transformation is introduced to effectively remove level effects, i.e. the state dependency of the...
The application of the continuous state space model to unequally spaced sequence data is discussed a...
In this paper, a method is introduced for approximating the likelihood for the unknown parameters of...
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