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2010
A very general model that subsumes a whole class of special cases of interest in much the same way that linear regression does is the state-space model or the dynamic linear model, which was introduced in Kalman [112] and Kalman and Bucy [113]. The model arose in the space tracking setting, where the state equation defines the motion equations for the ...
Robert H. Shumway, David S. Stoffer
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A very general model that subsumes a whole class of special cases of interest in much the same way that linear regression does is the state-space model or the dynamic linear model, which was introduced in Kalman [112] and Kalman and Bucy [113]. The model arose in the space tracking setting, where the state equation defines the motion equations for the ...
Robert H. Shumway, David S. Stoffer
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On the stability of 2D state‐space models
Numerical Linear Algebra with Applications, 2011SUMMARYIn this paper, we consider the problem of stability of two‐dimensional linear systems. New sufficient conditions for the asymptotic stability are derived in terms of linear matrix inequalities. Copyright © 2011 John Wiley & Sons, Ltd.
Djilali Bouagada, Paul Van Dooren
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2021
This chapter introduces state space models and provides some motivating examples. Linear Gaussian and non-linear, non-Gaussian models are introduced. Examples include linear trend and seasonal time series, time-varying regression, bearings-only tracking, financial time series and systems identification state space models. The chapter sets the stage for
Christiaan Heij +2 more
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This chapter introduces state space models and provides some motivating examples. Linear Gaussian and non-linear, non-Gaussian models are introduced. Examples include linear trend and seasonal time series, time-varying regression, bearings-only tracking, financial time series and systems identification state space models. The chapter sets the stage for
Christiaan Heij +2 more
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The likelihood for a state space model
Biometrika, 1988This paper derives an expression for the likelihood for a state space model. The expression can be evaluated with the Kalman filter initialized at a starting state estimate of zero and associated estimation error covariance matrix of zero. Adjustment for initial conditions can be made after filtering.
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2008
State space models is a rather loose term given to time series models, usually formulated in terms of unobserved components, that make use of the state space form for their statistical treatment.
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State space models is a rather loose term given to time series models, usually formulated in terms of unobserved components, that make use of the state space form for their statistical treatment.
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1996
In recent years state-space representations and the associated Kalman recursions have had a profound impact on time series analysis and many related areas.
Peter J. Brockwell, Richard A. Davis
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In recent years state-space representations and the associated Kalman recursions have had a profound impact on time series analysis and many related areas.
Peter J. Brockwell, Richard A. Davis
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2003
The state space modeling tools in S+FinMetrics are based on the algorithms in SsfPack 3.0 developed by Siem Jan Koopman and described in Koopman, Shephard and Doornik (1999, 2001)1. SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in state space form.
Eric Zivot, Jiahui Wang
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The state space modeling tools in S+FinMetrics are based on the algorithms in SsfPack 3.0 developed by Siem Jan Koopman and described in Koopman, Shephard and Doornik (1999, 2001)1. SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in state space form.
Eric Zivot, Jiahui Wang
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2013
The nonlinear systems under consideration in this paper are described by differential equations. In the same way as for linear systems, it has system state variables, inputs and outputs. The paper provides basic definitions for state space models of nonlinear systems, and tools for preliminary analysis, including linearisation around operating points ...
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The nonlinear systems under consideration in this paper are described by differential equations. In the same way as for linear systems, it has system state variables, inputs and outputs. The paper provides basic definitions for state space models of nonlinear systems, and tools for preliminary analysis, including linearisation around operating points ...
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2021
This chapter mentions the stochastic model and defines the state-space model as a stochastic model. We also explain the features and classification of the state-space model.
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This chapter mentions the stochastic model and defines the state-space model as a stochastic model. We also explain the features and classification of the state-space model.
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1991
State space models may be regarded as generalizations of the models considered so far. They have been used extensively in system theory, the physical sciences, and engineering. The terminology is therefore largely from these fields. The general idea behind these models is that an observed (multiple) time series y 1 ,…, y T depends upon a possibly ...
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State space models may be regarded as generalizations of the models considered so far. They have been used extensively in system theory, the physical sciences, and engineering. The terminology is therefore largely from these fields. The general idea behind these models is that an observed (multiple) time series y 1 ,…, y T depends upon a possibly ...
openaire +1 more source