Results 21 to 30 of about 720,413 (297)
Standard State Space Models of Unawareness (Extended Abstract) [PDF]
Electronic Proceedings in Theoretical Computer Science, 2016 The impossibility theorem of Dekel, Lipman and Rustichini has been thought to demonstrate that standard state-space models cannot be used to represent unawareness. We first show that Dekel, Lipman and Rustichini do not establish this claim.
Peter Fritz, Harvey Lederman
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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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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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Comparison of prediction using Matching Pattern and state space models [PDF]
المجلة العراقية للعلوم الاحصائية, 2022 Predicting future behavior is one of the important topics in statistical sciences due to the need for it in different areas of life, and most countries rely on their development programs on advanced scientific foundations and methods in order to reach ...
heyam hayawi, najlaa saad
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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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State-space models in estimating Lithuanian Business Cycle
Lietuvos Matematikos Rinkinys, 2005 There is not abstract.
Audronė Jakaitienė
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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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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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