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Activity Number: 360
Type: Contributed
Date/Time: Wednesday, August 6, 2008 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #300745
Title: Semiparametric Additive Isotonic Regression
Author(s): Guang Cheng*+
Companies: Statistical and Applied Mathematical Sciences Institute
Address: P.O. Box 14006, Research Triangle Park, NC, 27709-4006,
Keywords: Isotonic Regression ; Semiparametric Model ; Additive Regression ; Oracle Property
Abstract:

This paper is about the efficient estimation of the semiparametric additive isotonic regression model, i.e. $Y=X'\beta+\sum_{j=1}^{J}h_{j}(W_{j})+\epsilon$. Each additive component $h_{j}$ is assumed to be a monotone function. It is shown that the least square estimator of the parametric component is asymptotically normal. Moreover, the isotonic estimator for each additive functional component is proved to have the oracle property, which means it can be estimated with the highest asymptotic accuracy, equivalently, as if the other components were known.

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