TY - GEN A1 - Polzehl, Jörg A1 - Spokoiny, Vladimir T1 - Local likelihood modeling by adaptive weights smoothing N2 - 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. Y1 - 2004 UR - https://opus4.kobv.de/opus4-matheon/frontdoor/index/index/docId/69 UR - https://nbn-resolving.org/urn:nbn:de:0296-matheon-695 ER -