Journal article icon

Journal article

Sparse regularized local regression

Abstract:

The intention is to provide a Bayesian formulation of regularized local linear regression, combined with techniques for optimal bandwidth selection. This approach arises from the idea that only those covariates that are found to be relevant for the regression function should be considered by the kernel function used to define the neighborhood of the point of interest. However, the regression function itself depends on the kernel function. A maximum posterior joint estimation of the regression...

Expand abstract
Publication status:
Published

Actions


Access Document


Publisher copy:
10.1016/j.csda.2013.01.008

Authors


More by this author
Institution:
University of Oxford
Department:
Oxford, MSD, Psychiatry
Larranaga, P More by this author
Journal:
COMPUTATIONAL STATISTICS and DATA ANALYSIS
Volume:
62
Pages:
122-135
Publication date:
2013-06-05
DOI:
ISSN:
0167-9473
URN:
uuid:74615c12-ba66-4a0c-9de2-983819f780ab
Source identifiers:
385387
Local pid:
pubs:385387

Terms of use


Metrics



If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP