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Lazy lasso for local regression

Abstract:

Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear...

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Publisher copy:
10.1007/s00180-011-0274-0

Authors


Vidaurre, D More by this author
Larrañaga, P More by this author
Journal:
Computational Statistics
Volume:
27
Issue:
3
Pages:
531-550
Publication date:
2012-09-05
DOI:
EISSN:
1613-9658
ISSN:
0943-4062
URN:
uuid:7a7368bf-dbe7-4fb7-922f-34aa06dbab3e
Source identifiers:
364673
Local pid:
pubs:364673

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