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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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Comparison Prediction of Transfer Function Models and State Space Models Using Fuzzy Method [PDF]
المجلة العراقية للعلوم الاحصائية, 2021 The research aims to build dynamic models represented by the transfer function and State Space Models of a single input variable and a single output variable, The input and output variables are represented by the temperatures of the water before the ...
Fahad Subhy, Heyam Hayawi
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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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Model Uncertainty, State Uncertainty, and State-space Models [PDF]
, 2013 State-space models have been increasingly used to study macroeconomic and financial problems. A state-space representation consists of two equations, a measurement equation which links the observed variables to unobserved state variables and a transition
Young, ER +5 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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Bootstrap prediction intervals in state-space models [PDF]
, 2008 Prediction intervals in state-space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, with the true parameters substituted by consistent estimates. This approach has two limitations.
Ruiz Ortega, Esther +5 more
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Animals, 2022 The European turtle dove is an important game bird for the hunters in Greece, which is one of a few European countries where its hunting is allowed. The sustainability of the species’ hunting in Europe is discussed during the last several years due to ...
Christos Thomaidis +4 more
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Monte Carlo fixed-lag smoothing in state-space models [PDF]
Nonlinear Processes in Geophysics, 2014 This paper presents an algorithm for Monte Carlo fixed-lag smoothing in state-space models defined by a diffusion process observed through noisy discrete-time measurements.
A. Cuzol, E. Mémin
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On the State-Space Model of Unawareness
, 2023 working paper, please reference this version in further ...
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