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

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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