Upper bounds for the \(L_ 1\)-risk of the minimum \(L_ 1\)-distance regression estimator

DOI10.1007/BF00053402zbMath0772.62037OpenAlexW1494162474MaRDI QIDQ1260728

Marek Kaluszka, Lestaw Gajek

Publication date: 25 August 1993

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00053402

zbMATH Keywords

minimum distance estimationrates of convergencedependence structurebounded derivativesroughness penaltycommon varianceindependent errors of measurementL1-riskminimum L1-distance regression estimator

Mathematics Subject Classification ID

Density estimation (62G07) General nonlinear regression (62J02)

Related Items (1)

Expectation values and variance based on \(\mathcal L^p\)-norms

Cites Work

• Spline smoothing: The equivalent variable kernel method
• Estimating a regression function
• Estimating a density and its derivatives via the minimum distance method
• A Unifying Approach to Nonparametric Regression Estimation
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