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The Gaussian state space form
, 1994
"... In this paper we suggest the use of simulation techniques to extend the applicability of the usual Gaussian state space filtering and smoothing techniques to a class of nonGaussian time series models. This allows a fully Bayesian or maximum likelihood analysis of some interesting models, including ..."
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In this paper we suggest the use of simulation techniques to extend the applicability of the usual Gaussian state space filtering and smoothing techniques to a class of nonGaussian time series models. This allows a fully Bayesian or maximum likelihood analysis of some interesting models, including
Decomposition of time series models in statespace form
"... This paper gives a methodology for decompositions of a very wide class of time series, including normal and nonnormal time series, which are represented in statespace form. In particular the linked signals generated from dynamic generalized linear models are decomposed into a suitable sum of noise ..."
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This paper gives a methodology for decompositions of a very wide class of time series, including normal and nonnormal time series, which are represented in statespace form. In particular the linked signals generated from dynamic generalized linear models are decomposed into a suitable sum
The ARIMA model in state space form
, 2000
"... This article explores an alternative state space representation for ARIMA models to that usually advocated. The alternative representation has minimal state order. More importantly, it has more convenient Kalman filter convergence properties. This convergence reveals the concrete connection between ..."
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Cited by 1 (0 self)
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This article explores an alternative state space representation for ARIMA models to that usually advocated. The alternative representation has minimal state order. More importantly, it has more convenient Kalman filter convergence properties. This convergence reveals the concrete connection between
SUPPLEMENTARY MATERIAL A Formulation in statespace form
"... We now exemplify how the model structures in this article may be constructed from the information contained in Figures 2 and 3. Consider the upper left model in Figure 2, Mm,a. It consists of two state variables, describing the concentration of IR and IR · P · ins, respectively. Let these state vari ..."
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We now exemplify how the model structures in this article may be constructed from the information contained in Figures 2 and 3. Consider the upper left model in Figure 2, Mm,a. It consists of two state variables, describing the concentration of IR and IR · P · ins, respectively. Let these state
Fast estimation methods for time series models in statespace form
, 2005
"... We propose two fast, stable and consistent methods to estimate time series models expressed in their equivalent statespace form. They are useful both, to obtain adequate initial conditions for a maximumlikelihood iteration, or to provide final estimates when maximumlikelihood is considered inad ..."
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We propose two fast, stable and consistent methods to estimate time series models expressed in their equivalent statespace form. They are useful both, to obtain adequate initial conditions for a maximumlikelihood iteration, or to provide final estimates when maximumlikelihood is considered
MODELLING OF DYNAMIC MEASUREMENTS FOR UNCERTAINTY ANALYSIS BY MEANS OF DISCRETIZED STATESPACE FORMS
, 2009
"... Abstract − Both, the ISOGUM [1] and the Supplement S1 of the GUM [2] require expressing the knowledge about the measurement process by a socalled measurement function [3], which represents the mathematical relationship between the relevant parameters, the influence quantities, and the measurand(s) ..."
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possible modelling approach for dynamic measurements that utilizes discretized statespace forms. The basic role of the causeeffect approach and its necessary inversion for the uncertainty evaluation is emphasized. The paper is an extension and refinement of former work of the authors [4].
Normalized coprime factorizations for systems in generalized state space form
 IEEE Trans. on Automatic Control
, 1993
"... AbstractThis note presents a statespace algorithm for the calculation of a normalized coprime factorization of continuoustime generalized dynamical systems. It will be shown that two Riccati equations have to be solved to obtain this normalized coprime factorization. I. ..."
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Cited by 3 (0 self)
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AbstractThis note presents a statespace algorithm for the calculation of a normalized coprime factorization of continuoustime generalized dynamical systems. It will be shown that two Riccati equations have to be solved to obtain this normalized coprime factorization. I.
Forward path model predictive control using a nonminimal statespace form
, 2008
"... Abstract: This paper considers model predictive control (MPC) using a nonminimal statespace (NMSS) form, in which the state vector consists only of the directly measured system variables. Two control structures emerge from the analysis, namely the conventional feedback form and an alternative forw ..."
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Abstract: This paper considers model predictive control (MPC) using a nonminimal statespace (NMSS) form, in which the state vector consists only of the directly measured system variables. Two control structures emerge from the analysis, namely the conventional feedback form and an alternative
Inference for adaptive time series models: stochastic volatility and conditionally Gaussian state space form
, 2003
"... In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSFSV model. We show that conventional MCMC algorithms for this type of model are ineffective, but that this problem can be removed by reparameteri ..."
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Cited by 18 (5 self)
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In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSFSV model. We show that conventional MCMC algorithms for this type of model are ineffective, but that this problem can be removed
Results 1  10
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6,652