Frontier estimation via kernel regression on high power-transformed data

DOI10.1016/j.jmva.2006.11.006zbMath1206.62070arXiv1103.5956OpenAlexW1991967095MaRDI QIDQ2476143

Yanyan Li

Publication date: 11 March 2008

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1103.5956

zbMATH Keywords

kernel estimatorFrontier estimationpower-transform

Mathematics Subject Classification ID

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

Related Items (15)

Frontier estimation with kernel regression on high order moments ⋮ Nonparametric estimation in a regression model with additive and multiplicative noise ⋮ \(L_{1}\)-optimal linear programming estimator for periodic frontier functions with Hölder continuous derivative ⋮ Kernel estimation of extreme regression risk measures ⋮ On kernel smoothing for extremal quantile regression ⋮ Functional kernel estimators of large conditional quantiles ⋮ Estimation and hypotheses testing in boundary regression models ⋮ Estimating an endpoint with high order moments in the Weibull domain of attraction ⋮ Frontier estimation using kernel smoothing estimators with data transformation ⋮ Kernel estimators of extreme level curves ⋮ Estimation in Nonparametric Regression with Non-Regular Errors ⋮ On the Strong Consistency of the Kernel Estimator of Extreme Conditional Quantiles ⋮ Semi-parametric transformation boundary regression models ⋮ Frontier estimation with local polynomials and high power-transformed data ⋮ Estimating high quantiles based on dependent circular data

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