Decomposition of neurological multivariate time series by state space modelling
DOI10.1007/S11538-010-9563-YzbMath1221.92020OpenAlexW2021060043WikidataQ37785839 ScholiaQ37785839MaRDI QIDQ535574
Kin Foon Kevin Wong, Andreas Galka, Tohru Ozaki, Ulrich Stephani, Hiltrud Muhle
Publication date: 13 May 2011
Published in: Bulletin of Mathematical Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11538-010-9563-y
Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Neural biology (92C20) Biomedical imaging and signal processing (92C55)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Whitening as a tool for estimating mutual information in spatiotemporal data sets
- A dynamic factor model for the analysis of multivariate time series
- Time series: theory and methods
- Dynamic structural systems under indirect observation: Identifiability and estimation aspects from a system theoretic perspective
- Maximum likelihood and prediction error methods
- Tracking and data association
- Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes
- Estimating the dimension of a model
- Independent component analysis, a new concept?
- Geometrical structures of FIR manifold and multichannel blind deconvolution
- Generalized autoregressive conditional heteroscedasticity
- Sure success partial search
- GARCH modelling of covariance in dynamical estimation of inverse solutions
- Extraction of Specific Signals with Temporal Structure
- An innovation approach to non-Gaussian time series analysis
- Model Selection for Convolutive ICA with an Application to Spatiotemporal Analysis of EEG
- Indeterminacy and identifiability of blind identification
- A Note on the Extraction of Components from Time Series
- State Space and Unobserved Component Models
- Kalman Filtering
- Evaluation of likelihood functions for Gaussian signals
- Estimation of parameters and eigenmodes of multivariate autoregressive models
- Identification of stochastic linear dynamic systems using Kalman filter representation
- Applied Optimization
- A new look at the statistical model identification
This page was built for publication: Decomposition of neurological multivariate time series by state space modelling
