Nearly root-\(n\) approximation for regression quantile processes

DOI10.1214/12-AOS1021zbMath1284.62291arXiv1210.1092OpenAlexW2081314317MaRDI QIDQ693744

Stephen L. Portnoy

Publication date: 10 December 2012

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1210.1092

zbMATH Keywords

regression quantilesasymptotic approximationHungarian construction

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Linear inference, regression (62J99) Order statistics; empirical distribution functions (62G30) Functional limit theorems; invariance principles (60F17)

Related Items

The second-order bias of quantile estimators, A conversation with Stephen Portnoy, Smoothed quantile regression with large-scale inference, A direct approach to inference in nonparametric and semiparametric quantile models, On general notions of depth for regression, Quantile-Regression Inference With Adaptive Control of Size, Saddlepoint tests for quantile regression, Bahadur representations for bootstrap quantiles

Uses Software

• quantreg

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• On monotonicity of regression quantile functions
• Extensions of results of Komlós, Major, and Tusnády to the multivariate case
• Direct use of regression quantiles to construct confidence sets in linear models
• Tests of linear hypotheses based on regression rank scores
• An approximation of partial sums of independent RV's, and the sample DF. II
• An approximation of partial sums of independent RV'-s, and the sample DF. I
• Regression Quantiles
• Analytical and Bootstrap Approximations to Estimator Distributions in L 1 Regression
• A resampling method based on pivotal estimating functions
• Markov Chain Marginal Bootstrap
• Bootstrap Methods for Median Regression Models
• Tail Probability Approximations
• Probability Inequalities for Sums of Bounded Random Variables