CATS: Clustering after transformation and smoothing
成果类型：
Article
署名作者：
Serban, N; Wasserman, L
署名单位：
Carnegie Mellon University
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1198/016214504000001574
发表日期：
2005
页码：
990-999
关键词：
摘要：
CATS-clustering after transformation and smoothing-is a technique for nonparametrically estimating and clustering a large number of curves. Our motivating example is a genetic microarray experiment, but the method is very general. The method includes transformation and smoothing multiple curves, multiple nonparametric testing for screening out flat curves, clustering curves with similar shape, and nonparametrically inferring the clustering estimation error rate.