Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11222-009-9139-6
Title: A sign based loss approach to model selection in nonparametric regression
Authors: Nott, D.J. 
Jialiang, L. 
Keywords: Bayesian nonparametric regression
Bayesian variable selection
False selection rate
Generalized additive model
Issue Date: 2010
Citation: Nott, D.J., Jialiang, L. (2010). A sign based loss approach to model selection in nonparametric regression. Statistics and Computing 20 (4) : 485-498. ScholarBank@NUS Repository. https://doi.org/10.1007/s11222-009-9139-6
Abstract: In parametric regression models the sign of a coefficient often plays an important role in its interpretation. One possible approach to model selection in these situations is to consider a loss function that formulates prediction of the sign of a coefficient as a decision problem. Taking a Bayesian approach, we extend this idea of a sign based loss for selection to more complex situations. In generalized additive models we consider prediction of the sign of the derivative of an additive term at a set of predictors. Being able to predict the sign of the derivative at some point (that is, whether a term is increasing or decreasing) is one approach to selection of terms in additive modelling when interpretation is the main goal. For models with interactions, prediction of the sign of a higher order derivative can be used similarly. There are many advantages to our sign-based strategy for selection: one can work in a full or encompassing model without the need to specify priors on a model space and without needing to specify priors on parameters in submodels. Also, avoiding a search over a large model space can simplify computation. We consider shrinkage prior specifications on smoothing parameters that allow for good predictive performance in models with large numbers of terms without the need for selection, and a frequentist calibration of the parameter in our sign-based loss function when it is desired to control a false selection rate for interpretation. © 2009 Springer Science+Business Media, LLC.
Source Title: Statistics and Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/104971
ISSN: 09603174
DOI: 10.1007/s11222-009-9139-6
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

6
checked on Nov 12, 2019

WEB OF SCIENCETM
Citations

4
checked on Jul 3, 2019

Page view(s)

105
checked on Nov 9, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.