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Abstract Details

Activity Number: 114
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #304801
Title: Partially Linear Structure Identification in Generalized Additive Models with NP-Dimensionality
Author(s): Pang Du*+ and Heng Lian and Hua Liang
Companies: Virginia Tech and National University of Singapore and University of Rochester
Address: 410 Hutcheson Hall 0439, Blacksburg, VA, 24061, United States
Keywords: Splines ; Quasi-likelihood ; SCAD penalty ; Partially linear models ; NP-dimensionality

Separation of the parametric and nonparametric components in additive models based on penalized likelihood has received attention recently. However, it remains unknown whether consistent separation is possible in generalized additive models, and how high dimensionality the method can handle. In this article, we study the doubly SCAD-penalized approach for problems of non-polynomial (NP) dimensionality and demonstrate its oracle property.

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