Abstract #301369

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JSM 2003 Abstract #301369
Activity Number: 331
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #301369
Title: Local Likelihood Modeling by Adaptive Weights Smoothing
Author(s): Joerg Polzehl*+ and Vladimir Spokoiny
Companies: Weierstrass Institute and Humboldt University
Address: Analysis und Stochastik, Berlin, , D-10117, Germany
Keywords: adaptive estimation ; local likelihood ; density estimation ; volatility ; classification ; tail index estimation
Abstract:

The paper presents a unified approach to local likelihood estimation for a broad class of nonparametric models, including, e.g., the regression, density, Poisson, and binary response model. The method extends the adaptive weights smoothing (AWS) procedure introduced in Polzehl and Spokoiny (2000) in the context of image denoising. The performance of the proposed procedure is illustrated by a number of numerical examples and applications to density or volatility estimation, classification and estimation of the tail index parameter.

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