Results 261 to 270 of about 963,951 (309)
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2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019
V1:8 pages, 4 figures.
Simon J. Godsill +2 more
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V1:8 pages, 4 figures.
Simon J. Godsill +2 more
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Benchmarking by State Space Models
International Statistical Review / Revue Internationale de Statistique, 1997SummaryWe have a monthly series of observations which are obtained from sample surveys and are therefore subject to survey errors. We also have a series of annual values, called benchmarks, which are either exact or are substantially more accurate than the survey observations; these can be either annual totals or accurate values of the underlying ...
Durbin, J., Quenneville, B.
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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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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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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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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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STATE TRANSITION SPECIFICATION IN STATE‐SPACE MODELS
Journal of Time Series Analysis, 1986Abstract.This note investigates an approach to state transition specification in statespace models. The approach generalizes the procedure whereby first or higher order differences in the state are modelled as white noise.
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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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