State space models
3.1 State Space Models
... resulting state space models may or may not contain all the modes of the original transfer function, where by transfer function modes we mean poles of the original transfer function (before zero-pole ...
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Dynamic State Space Models
... Keywords: state-space models, signal extraction, unobserved ...dynamic state-space model was developed in the control systems literature, where physi- cal systems are described ...
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Linear State Space Models
... ❖ Stability and natural response characteristics of the system can be studied from the eigenvalues of the matrix A or (A q , A δ ). ❖ State space models faciclitate the study of certain system ...
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The state space models toolbox for MATLAB
... Abstract State Space Models (SSM) is a MATLAB toolbox for time series analysis by state space ...of models, with support for univariate and multivariate models, complex ...
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State space models with spatial deformation
... Abstract Space deformation has been proposed to model space-time varying observation processes with non-stationary spatial covariance structure under the hypothesis of temporal ...through state ...
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The STAMP Software for State Space Models
... using state-space methods with unobserved ...multivariate state-space models, provides a wide array of diagnostics, and has a batch mode ...of models available in STAMP, and that ...
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Cointegration Analysis with State Space Models
... with state space models, where both structure theoretic as well as statistical aspects are ...with state space models a fully fledged alternative to VAR cointegration analysis ...
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Applications of and extensions to state-space models
... Discussion State-space models, or more generally directed graphical models, provide a powerful and flexible framework for modeling dynamic ...Gaussian state-space model for ...
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Fitting State Space Models with EViews
... homoscedasticity and normality (based on Commandeur and Koopman 2007 , p. 90–96). Results are summarized in Table 1 . They indicate that all of the model assumptions are satisfied. Although EViews allows storing all ...
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Models for optimising the theta method and their relationship to state space models
... a state space model that selects the best short-term theta line optimally and revises the long-term theta line ...to state space models for simple exponential smoothing with a ...
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Models for optimising the theta method and their relationship to state space models
... a state space model that selects the best short-term theta line optimally and revises the long-term theta line ...to state space models for simple exponential smoothing with a ...
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State Space Models and Inverse FORM - A Review
... to state space models poses several new ...include state space models and compute the uncertainty in the RUL ...with state space models, and develops ...
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Robust estimation of linear state space models
... linear state space models are typically estimated with maximum likelihood estimation, where the likelihood is computed analytically with the Kalman ...regression models is considered as a ...
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Bootstrap prediction intervals in State Space models
... in State Space models can be obtained by assum- ing Gaussian innovations and using the prediction equations of the Kalman filter, where the true parameters are substituted by consistent ...to ...
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State Space Models with Endogenous Regime Switching
... of state space models whose parameters are switching endogenously between two regimes, depending on whether an autoregressive latent factor crosses some threshold ...of state space ...
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Multiplicative State Space Models for Intermittent Time Series
... statistical models, which limits their ...general state-space model that takes intermittence of data into account, extending the taxonomy of exponential smoothing ...non-intermittent state ...
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Stochastic Gradient MCMC for Nonlinear State Space Models
... Abstract State space models (SSMs) provide a flexible framework for modeling complex time series via a latent stochastic ...Markov models (HMMs) and linear SSMs using buffered stochastic ...
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Efficient Bayesian estimation of multivariate state space models
... 4 Empirical Illustration 4.1 Data Description The Bayesian methodology for estimating multivariate state space models is used to analyse MODIS satellite image data. In particular, a multivariate time ...
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Prediction Intervals for Exponential Smoothing State Space Models.
... of state space models, we have provided derivations of exact matrix expressions for the means and variances of prediction ...of state space models to the commonly used ...
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On Particle Methods for Parameter Estimation in State-Space Models
... in state- space models, consider first the scenario where the parameter θ is ...the state process {X n } given the observations {Y n } is only feasible analytically for simple models ...
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