Regression spline smoothing using the minimum description length principle

DOI10.1016/S0167-7152(99)00191-1zbMath0963.62036OpenAlexW2143869582MaRDI QIDQ1567321

Thomas C. M. Lee

Publication date: 3 July 2001

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-7152(99)00191-1

zbMATH Keywords

minimum description lengthregression splineautomatic knot selectionregression spline smoothing

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Statistical aspects of information-theoretic topics (62B10)

Related Items (3)

An Introduction to Coding Theory and the Two-Part Minimum Description Length Principle ⋮ Geometrically designed, variable knot regression splines ⋮ Spatial interpolation of high-frequency monitoring data

Cites Work

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• Nonparametric regression using Bayesian variable selection
• A comparison of regression spline smoothing procedures
• Adapting to Unknown Smoothness via Wavelet Shrinkage
• On stochastic complexity and nonparametric density estimation
• Flexible Parsimonious Smoothing and Additive Modeling
• Automatic Bayesian Curve Fitting
• Hybrid Adaptive Splines

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