Results 21 to 30 of about 687,122 (255)
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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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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2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 V1:8 pages, 4 figures.
Simon J. Godsill +2 more
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Formulating State Space Models in R with Focus on Longitudinal Regression Models
Journal of Statistical Software, 2006 We provide a language for formulating a range of state space models with response densities within the exponential family. The described methodology is implemented in the R-package sspir. A state space model is specified similarly to a generalized linear
Claus Dethlefsen +1 more
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KFAS: Exponential Family State Space Models in R
Journal of Statistical Software, 2017 State space modeling is an efficient and flexible method for statistical inference of a broad class of time series and other data. This paper describes the R package KFAS for state space modeling with the observations from an exponential family, namely ...
Jouni Helske
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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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Variational Bayesian inference of linear state space models
The Journal of Engineering, 2019 This article studies a variational Bayesian method to fix the linear regression (LR) model of which regressors are Gaussian distributed with non-zero prior means, and then apply the method to the linear state space (LSS) model.
Chuanchao Pan +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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