Add to ORKGThe application of the continuous state space model to unequally spaced sequence data is discussed and illustrated. The continuous model implies a discrete model for the observed data. Practical expressions for relevant discrete model quantities are given. These quantites are required for the digital processing of the data and in particular for the application of the Kalman and smoothing filter and related calculations. Applications illustrate the procedures.19 page(s
The exact discrete model satisfied by equispaced data generated by a linear stochastic differential ...
We discuss two separate techniques for Kalman Filtering in the presence of state space equality cons...
A method for semiparametric smoothing of discrete data is proposed. The method consists of the repea...
AbstractThe paper reviews and generalizes recent filtering and smoothing algorithms for observations...
Continuous-discrete state space models, Stochastic differential equations, Itô calculus, Sampling, K...
Serial correlation in the within subject error structure in longitudinal data with unequally spaced ...
State-space smoothing has found many applications in science and engineering. Under linear and Gauss...
The problem of state estimation of a linear, dynamical state-space system where the output is subjec...
The paper discusses techniques for analysis of sequential data from variable processes, particularly...
This paper gives a new approach to diffuse filtering and smoothing for multivariate state space mode...
State space model is a class of models where the observations are driven by underlying stochastic pr...
This contribution reviews theory, algorithms, and validation results for system identification of co...
We present algorithms for computing the weights implicitly assigned to observations when es-timating...
In this paper we present methods for fixed-lag smoothing using Sequential Importance sampling (SIS) ...
In this paper, a method is introduced for approximating the likelihood for the unknown parameters of...
The exact discrete model satisfied by equispaced data generated by a linear stochastic differential ...
We discuss two separate techniques for Kalman Filtering in the presence of state space equality cons...
A method for semiparametric smoothing of discrete data is proposed. The method consists of the repea...
AbstractThe paper reviews and generalizes recent filtering and smoothing algorithms for observations...
Continuous-discrete state space models, Stochastic differential equations, Itô calculus, Sampling, K...
Serial correlation in the within subject error structure in longitudinal data with unequally spaced ...
State-space smoothing has found many applications in science and engineering. Under linear and Gauss...
The problem of state estimation of a linear, dynamical state-space system where the output is subjec...
The paper discusses techniques for analysis of sequential data from variable processes, particularly...
This paper gives a new approach to diffuse filtering and smoothing for multivariate state space mode...
State space model is a class of models where the observations are driven by underlying stochastic pr...
This contribution reviews theory, algorithms, and validation results for system identification of co...
We present algorithms for computing the weights implicitly assigned to observations when es-timating...
In this paper we present methods for fixed-lag smoothing using Sequential Importance sampling (SIS) ...
In this paper, a method is introduced for approximating the likelihood for the unknown parameters of...
The exact discrete model satisfied by equispaced data generated by a linear stochastic differential ...
We discuss two separate techniques for Kalman Filtering in the presence of state space equality cons...
A method for semiparametric smoothing of discrete data is proposed. The method consists of the repea...