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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Neurochemical levels measured by brain MR spectroscopy (MRS) have been proposed as endpoints for clinical trials in early‐stage spinocerebellar ataxia (SCA) trials. We tested their trial‐readiness by quantifying neurochemicals in three affected brain regions in early‐stage cohorts of SCA2 and SCA3, examining their reproducibility in ...
James M. Joers +19 more
wiley +1 more source
T1 Over Squared Proton Density Ratio to Characterize Multiple Sclerosis Lesions
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Differentiating remyelinated from demyelinated lesions in MS remains challenging without histological confirmation. This study introduces the T1‐to‐PD2 ratio (TPR) imaging approach and evaluates its ability to characterize MS lesions alongside other quantitative MRI (qMRI) metrics. Methods Thirty individuals with MS (mean age: 47.5 ±
Sarah J. Wright +10 more
wiley +1 more source
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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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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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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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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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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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1998
In Chapter 1 we argued that mathematical models obtained from first principles usually contain latent variables. Up to now these latent variables did not enter the mathematical development. In Chapters 5 and 6 latent variable systems will be pursued in full generality. In the present chapter we discuss a special and important class of latent variables,
Jan Willem Polderman, Jan C. Willems
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In Chapter 1 we argued that mathematical models obtained from first principles usually contain latent variables. Up to now these latent variables did not enter the mathematical development. In Chapters 5 and 6 latent variable systems will be pursued in full generality. In the present chapter we discuss a special and important class of latent variables,
Jan Willem Polderman, Jan C. Willems
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