B spline variable selection for the single index models

B spline variable selection for the single index models Through the nonconcave penalized least squares method, we consider the variable selection in the full nonparametric regression models with the B spline-based single index approximation. Under some regular conditions, we show that the resulting estimates with SCAD and HARD thresholding penalties enjoy $$\sqrt{n}$$ n -consistency and oracle properties. We use some simulation studies and a real example to illustrate the performance of our proposed variable selection procedure. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistical Papers Springer Journals

B spline variable selection for the single index models

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Subject
Statistics; Statistics for Business/Economics/Mathematical Finance/Insurance; Probability Theory and Stochastic Processes; Economic Theory/Quantitative Economics/Mathematical Methods; Operations Research/Decision Theory
ISSN
0932-5026
eISSN
1613-9798
D.O.I.
10.1007/s00362-015-0721-z
Publisher site
See Article on Publisher Site

Abstract

Through the nonconcave penalized least squares method, we consider the variable selection in the full nonparametric regression models with the B spline-based single index approximation. Under some regular conditions, we show that the resulting estimates with SCAD and HARD thresholding penalties enjoy $$\sqrt{n}$$ n -consistency and oracle properties. We use some simulation studies and a real example to illustrate the performance of our proposed variable selection procedure.

Journal

Statistical PapersSpringer Journals

Published: Oct 30, 2015

References

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