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Parameter and state estimator for state space models. [PDF]
ScientificWorldJournal, 2014 This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least ...
Ding R, Zhuang L.
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Wavelets in state space models [PDF]
Applied Stochastic Models in Business and Industry, 2003 AbstractIn this paper, we consider the utilization of wavelets in conjunction with state space models. Specifically, the parameters in the system matrix are expanded in wavelet series and estimated via the Kalman Filter and the EM algorithm. In particular this approach is used for switching models.
Zandonade, Eliana, Morettin, Pedro A.
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Bayesian State Space Models in Macroeconometrics [PDF]
SSRN Electronic Journal, 2020 AbstractState space models play an important role in macroeconometric analysis and the Bayesian approach has been shown to have many advantages. This paper outlines recent developments in state space modelling applied to macroeconomics using Bayesian methods.
Joshua C.C. Chan, Rodney W. Strachan
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CoRR, 2023 This paper studies sequence modeling for prediction tasks with long range dependencies. We propose a new formulation for state space models (SSMs) based on learning linear dynamical systems with the spectral filtering algorithm (Hazan et al. (2017)).
Naman Agarwal +3 more
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Graphical State Space Model [PDF]
2021 IEEE International Conference on Unmanned Systems (ICUS), 2021 In this paper, a new framework, named as graphical state space model, is proposed for the real time optimal estimation of a class of nonlinear state space model. By discretizing this kind of system model as an equation which can not be solved by Extended Kalman filter, factor graph optimization can outperform Extended Kalman filter in some cases.
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Modeling Volatility Using State Space Models [PDF]
International Journal of Neural Systems, 1997 In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of
Jens Timmer, Andreas S. Weigend
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Granger causality for state-space models [PDF]
Physical Review E, 2015 Granger causality, a popular method for determining causal influence between stochastic processes, is most commonly estimated via linear autoregressive modeling. However, this approach has a serious drawback: if the process being modeled has a moving average component, then the autoregressive model order is theoretically infinite, and in finite sample ...
Barnett, Lionel, Seth, Anil K.
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A STATE SPACE MODEL OF THE ECONOMIC FUNDAMENTALS [PDF]
Computers & Mathematics with Applications, 1989 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Craine, Roger, Bowman, David
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State‐space models for optical imaging [PDF]
Statistics in Medicine, 2007 AbstractMeasurement of stimulus‐induced changes in activity in the brain is critical to the advancement of neuroscience. Scientists use a range of methods, including electrode implantation, surface (scalp) electrode placement, and optical imaging of intrinsic signals, to gather data capturing underlying signals of interest in the brain.
Kary L, Myers +2 more
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Bellman Filtering for State-Space Models [PDF]
SSRN Electronic Journal, 2020 This article presents a filter for state-space models based on Bellman's dynamic programming principle applied to the mode estimator. The proposed Bellman filter generalises the Kalman filter including its extended and iterated versions, while remaining equally inexpensive computationally.
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