时空数据挖掘过程：LASR
Spatial-temporal data mining procedure: LASR
摘要：
本文关注的是我们的时空数据挖掘程序，LASR（发音为“激光”）的统计发展。LASR是大- $p$ -小- $n$数据自我配准纵向分析的缩写。它的动机是一项“神经肌肉电刺激”实验的研究，其中数据是嘈杂和异质的，可能不会从一个会话到另一个会话对齐，并且涉及大量的多重比较。LASR的三个主要组成部分是：（1）用于分离异质数据和区分异常值的数据分割，（2）用于空间和时间数据配准的自动方法，以及（3）用于基于错误发现率控制的$p$地图和电影识别"激活“区域的统计平滑映射。每一个组成部分本身都是令人感兴趣的。作为一种统计集合，LASR的思想适用于NMES实验之外的其他类型的时空数据集。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
