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Activity Number:
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447
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #301646 |
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Title:
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AdaSmoothing: Adaptive Smoothing Spline and Its Applications
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Author(s):
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Aijun Zhang*+
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Companies:
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The University of Michigan
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Address:
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439 West Hall, 1085 South University, Ann Arbor, MI, 48109,
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Keywords:
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AdaSmoothing ; nonparametric regression ; credit risk ; stochastic volatility
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Abstract:
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Smoothing spline is among the most popular nonparametric regression methods for function estimation, and it has been derived elegantly based on the theory of reproducing kernel Hilbert space. In this paper, we study the adaptive version of smoothing spline by making the smoothing parameter locally adaptive to the nonhomogeneous degree of smoothness at different locations. We show that the shape of such adaptive parameter is determined by the stochastic volatility of a derivative process. As an application, we give an example of using AdaSmoothing in modeling loss-given-default for credit risk portfolio.
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