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.Comment: Published 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