标题： 时空数据挖掘过程：LASR 显示英文标题

标题： Spatial-temporal data mining procedure: LASR

摘要： 本文关注我们时空数据挖掘过程LASR（发音为“激光”）的统计发展。 LASR是“纵向分析与大规模-$p$-小规模-$n$数据自注册”的缩写。 它源于对“神经肌肉电刺激”实验的研究，其中数据噪声大且异质，可能在不同会话之间不对齐，并涉及大量多重比较。 LASR的三个主要组成部分是：(1) 数据分割，用于分离异质数据和区分异常值，(2) 自动的空间和时间数据配准方法，以及(3) 基于假发现率控制的$p$-图和影片进行识别“激活”区域的统计平滑映射。 每个组成部分本身都有研究价值。 作为一个统计集合，LASR的思想适用于超越NMES实验的数据集类型的其他类型的空间-时间数据集。 显示英文摘要

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.