Segmentation of the mean of heteroscedastic data via cross-validation

被引：0
作者：
Sylvain Arlot
Alain Celisse
机构：
[1] Willow Project-Team,
[2] Laboratoire d’Informatique de l’Ecole Normale Superieure,undefined
[3] CNRS/ENS/INRIA UMR 8548,undefined
[4] Laboratoire de Mathématique Paul Painlevé UMR 8524 CNRS,undefined
[5] Université Lille 1,undefined
来源：
Statistics and Computing | 2011年 / 21卷
关键词：
Change-point detection; Resampling; Cross-validation; Model selection; Heteroscedastic data; CGH profile segmentation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper tackles the problem of detecting abrupt changes in the mean of a heteroscedastic signal by model selection, without knowledge on the variations of the noise. A new family of change-point detection procedures is proposed, showing that cross-validation methods can be successful in the heteroscedastic framework, whereas most existing procedures are not robust to heteroscedasticity. The robustness to heteroscedasticity of the proposed procedures is supported by an extensive simulation study, together with recent partial theoretical results. An application to Comparative Genomic Hybridization (CGH) data is provided, showing that robustness to heteroscedasticity can indeed be required for their analysis.
引用
收藏
页码：613 / 632
页数：19
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