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Title: | Finite Information Limit Variance-covariance Structures: Is the Entire Dataset Needed for Analysis? | Authors: | NASSIRI, Vahid MOLENBERGHS, Geert VERBEKE, Geert |
Issue Date: | 2016 | Publisher: | IEEE | Source: | Smari, W.W. (Ed.). 2016 International Conference on High Performance Computing & Simulation (HPCS), IEEE,p. 736-742 | Abstract: | Finite Information Limit (FIL) variance-covariance structures for hierarchical data are introduced and examined: for such data, it is often possible to analyze only a sometimes very small subset, leading to considerable computation time gain, with almost no efficiency loss. A central example is compound-symmetry. A simple approach is proposed to detect this property in a given dataset. | Notes: | [Nassiri, Vahid; Verbeke, Geert] Katholieke Univ Leuven, I BioStat, B-3000 Louvain, Belgium. [Molenberghs, Geert] Univ Hasselt, I BioStat, Hasselt, Belgium. [Molenberghs, Geert] Katholieke Univ Leuven, Leuven, Belgium. [Verbeke, Geert] Univ Hasselt, Hasselt, Belgium. | Keywords: | Compound-symmetry; Correlated Data; Data sub-sampling; Fast and parallel computation;fast and parallel computation; compound-symmetry; correlated data; data subsampling | Document URI: | http://hdl.handle.net/1942/23265 | ISBN: | 9781509020881 | DOI: | 10.1109/HPCSim.2016.7568408 | ISI #: | 000389590600100 | Rights: | ©2016 IEEE | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2018 |
Appears in Collections: | Research publications |
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10.1109@hpcsim.2016.7568408.pdf Restricted Access | Published version | 459.95 kB | Adobe PDF | View/Open Request a copy |
Finite Information Limit Variance-covariance Structures Is the Entire Dataset Needed for Analysis.pdf Restricted Access | Peer-reviewed author version | 459.95 kB | Adobe PDF | View/Open Request a copy |
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