- Abstract:
-
We congratulate Lee, Nadler and Wasserman (henceforth LNW) on a very interesting paper on new methodology and supporting theory [arXiv:0707.0481]. Treelets seem to tackle two important problems of modern data analysis at once. For datasets with many variables, treelets give powerful predictions even if variables are highly correlated and redundant. Maybe more importantly, interpretation of the results is intuitive. Useful insights about relevant groups of variables can be gained. Our comments...
Expand abstract - Publication status:
- Published
- Publisher copy:
- 10.1214/08-AOAS137C
-
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- Journal:
- Annals of Applied Statistics
- Volume:
- 2
- Issue:
- 2
- Pages:
- 478-481
- Publication date:
- 2008-07-25
- DOI:
- EISSN:
-
1941-7330
- ISSN:
-
1932-6157
- URN:
-
uuid:253858b4-26b3-4c8f-a639-1a599e0727fd
- Source identifiers:
-
97836
- Local pid:
- pubs:97836
- Language:
- English
- Keywords:
- Copyright date:
- 2008
- Notes:
-
Published in at http://dx.doi.org/10.1214/08-AOAS137C the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org)
Journal article
Discussion of: Treelets--An adaptive multi-scale basis for sparse unordered data
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