Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s11222-010-9196-x | Statistics and Computing |
Keywords | DocType | Volume |
Change-point detection,Resampling,Cross-validation,Model selection,Heteroscedastic data,CGH profile segmentation | Journal | 20 |
Issue | ISSN | Citations |
2 | 0960-3174 | 8 |
PageRank | References | Authors |
0.62 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sylvain Arlot | 1 | 65 | 6.87 |
| Alain Celisse | 2 | 34 | 2.82 |