MDL Mean Function Selection in Semiparametric Kernel Regression Models

DOI10.1080/03610920701875267zbMath1143.62024OpenAlexW3122243502MaRDI QIDQ3526078

Jan G. De Gooijer, Ao Yuan

Publication date: 24 September 2008

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: http://papers.tinbergen.nl/08046.pdf

zbMATH Keywords

semiparametric modelkernel density estimatormaximum likelihood estimatornonlinear regressionminimum description length

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20)

Cites Work

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• Stochastic complexity and modeling
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• Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator
• Model Selection Using the Minimum Description Length Principle
• Model Selection and the Principle of Minimum Description Length
• Schwarz, Wallace, and Rissanen: Intertwining Themes in Theories of Model Selection
• Fisher information and stochastic complexity

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