Spatial-temporal data mining procedure: LASR

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dc.creatorWang, Xiaofeng
dc.creatorSun, Jiayang
dc.creatorBogie, Kath
dc.date2006-11-24
dc.date.accessioned2026-07-07T08:08:26Z
dc.date.available2026-07-07T08:08:26Z
dc.descriptionThis paper is concerned with the statistical development of our spatial-temporal data mining procedure, LASR (pronounced ``laser''). LASR is the abbreviation for Longitudinal Analysis with Self-Registration of large-$p$-small-$n$ data. It was motivated by a study of ``Neuromuscular Electrical Stimulation'' experiments, where the data are noisy and heterogeneous, might not align from one session to another, and involve a large number of multiple comparisons. The three main components of LASR are: (1) data segmentation for separating heterogeneous data and for distinguishing outliers, (2) automatic approaches for spatial and temporal data registration, and (3) statistical smoothing mapping for identifying ``activated'' regions based on false-discovery-rate controlled $p$-maps and movies. Each of the components is of interest in its own right. As a statistical ensemble, the idea of LASR is applicable to other types of spatial-temporal data sets beyond those from the NMES experiments.
dc.descriptionPublished at http://dx.doi.org/10.1214/074921706000000707 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0611722
dc.identifierhttp://arxiv.org/abs/math/0611722
dc.identifierIMS Lecture Notes--Monograph Series 2006, Vol. 50, 213-231
dc.identifierdoi:10.1214/074921706000000707
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131265
dc.subjectStatistics Theory
dc.subject60K35, 60K35 (Primary) 60K35 (Secondary)
dc.titleSpatial-temporal data mining procedure: LASR
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