Journal article
Discussion of: Treelets--An adaptive multi-scale basis for sparse unordered data
- 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...
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- Publication status:
- Published
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- Publisher copy:
- 10.1214/08-AOAS137C
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Authors
Bibliographic Details
- Journal:
- Annals of Applied Statistics
- Volume:
- 2
- Issue:
- 2
- Pages:
- 478-481
- Publication date:
- 2008-07-25
- DOI:
- EISSN:
-
1941-7330
- ISSN:
-
1932-6157
- Source identifiers:
-
97836
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
pubs:97836
- UUID:
-
uuid:253858b4-26b3-4c8f-a639-1a599e0727fd
- Local pid:
- pubs:97836
- Deposit date:
- 2012-12-19
Terms of use
- 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)
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