WIAS Preprint No. 787, (2002)
Local likelihood modeling by adaptive weights smoothing
Authors
- Polzehl, Jörg
ORCID: 0000-0001-7471-2658 - Spokoiny, Vladimir
ORCID: 0000-0002-2040-3427
2010 Mathematics Subject Classification
- 62G08
Keywords
- adaptive weights, local likelihood, exponential family, density estimation, volatility, tail index, classification
DOI
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 context of image denoising. Performance of the proposed procedure is illustrated by a number of numerical examples and applications to estimation of the tail index parameter, classification, density and volatility estimation.
Appeared in
- Probab. Theory Related Fields, 135 (2006) pp. 335--362.
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