Name
Abstract | ||
|---|---|---|
We consider the challenges in estimating the state-related changes in brain connectivity networks with a large number of nodes. Existing studies use the sliding-window analysis or time-varying coefficient models, which are unable to capture both smooth and abrupt changes simultaneously, and rely on ad-hoc approaches to the high-dimensional estimation. To overcome these limitations, we propose a Ma... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMI.2017.2780185 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Brain modeling,Load modeling,Reactive power,Hidden Markov models,Covariance matrices,Estimation | Autoregressive model,Mathematical optimization,Subspace topology,Dynamic factor,Algorithm,Mean squared error,Factor analysis,Cluster analysis,Principal component analysis,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
37 | 4 | 0278-0062 |
Citations | PageRank | References |
3 | 0.39 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chee-Ming Ting | 1 | 72 | 13.17 |
| Hernando Ombao | 2 | 98 | 18.00 |
| S. Balqis Samdin | 3 | 20 | 4.57 |
| S. Hussain | 4 | 47 | 9.46 |