Abstract
In this article, we study a general single-index model with diverging number of predictors by using the adaptive Elastic-Net inverse regression method. The proposed method not only can estimate the direction of index and select important variables simultaneously, but also can avoid to estimate the unknown link function through nonparametric method. Under some regularity conditions, we show that the proposed estimators enjoy the so-called oracle property.
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© 2011 Springer-Verlag Berlin Heidelberg
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Li, X., Li, G., Yang, S. (2011). Adaptive Elastic-Net for General Single-Index Regression Models. In: Li, S., Wang, X., Okazaki, Y., Kawabe, J., Murofushi, T., Guan, L. (eds) Nonlinear Mathematics for Uncertainty and its Applications. Advances in Intelligent and Soft Computing, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22833-9_62
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DOI: https://doi.org/10.1007/978-3-642-22833-9_62
Publisher Name: Springer, Berlin, Heidelberg
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