Finite Information Limit Variance-covariance Structures: Is the Entire Dataset Needed for Analysis?

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23265

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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