Asymptotics of the “minimumL 1-norm” estimates in nonparametric regression models

DOI10.1007/BF02560718zbMath0807.62036OpenAlexW1561117736MaRDI QIDQ4319234

Ping Cheng, Peide Shi

Publication date: 5 March 1995

Published in: Acta Mathematica Sinica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02560718

zbMATH Keywords

nonparametric regressionoptimal rates of convergencepiecewise polynomial L1-norm estimator

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) General nonlinear regression (62J02)

Cites Work

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• Strong consistency and exponential rate of the ``minimum \(L_ 1\)-norm estimates in linear regression models
• Additive regression and other nonparametric models
• Asymptotically optimal selection of a piecewise polynomial estimator of a regression function
• Optimal rates of convergence for nonparametric estimators
• The consistency of nonlinear regression minimizing the \(L_ 1-\)norm
• Multivariate adaptive regression splines
• Optimal global rates of convergence for nonparametric regression
• A Maximum Likelihood Method for Piecewise Regression Models with a Continuous Dependent Variable
• Convergence of stochastic processes

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