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Local likelihood modeling by adaptive weights smoothing

Please always quote using this URN:urn:nbn:de:0296-matheon-695
  • 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 context of image denoising. 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. We also establish a number of important theoretical results on properties of the proposed procedure.

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Metadaten
Author:Jörg Polzehl, Vladimir Spokoiny
URN:urn:nbn:de:0296-matheon-695
Referee:Christof Schütte
Document Type:Preprint, Research Center Matheon
Language:English
Date of first Publication:2004/01/02
Release Date:2004/01/29
Institute:Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS)
Preprint Number:60
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