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VOL. 50 | 2006 Spatial-temporal data mining procedure: LASR
Xiaofeng Wang, Jiayang Sun, Kath Bogie

Editor(s) Jiayang Sun, Anirban DasGupta, Vince Melfi, Connie Page

## Abstract

This 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.

## Information

Published: 1 January 2006
First available in Project Euclid: 28 November 2007

zbMATH: 1268.60123
MathSciNet: MR2409554

Digital Object Identifier: 10.1214/074921706000000707

Subjects:
Primary: 60K35

Keywords: FDR under dependence , pressure sores , registration , segmentation , simultaneous inferences , spatial-temporal data , statistical smoothing mapping , wheelchair users