Change-point in stochastic design regression and the bootstrap

DOI10.1214/11-AOS874zbMath1220.62043arXiv1101.1032MaRDI QIDQ638805

Emilio Seijo, Bodhisattva Sen

Publication date: 14 September 2011

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

Full work available at URL: https://arxiv.org/abs/1101.1032

zbMATH Keywords

semiparametric regressionsmoothed bootstrapnonstandard asymptoticsargmax continuous mapping theoremconsistency of the bootstrapm out of n bootstrap

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Nonparametric tolerance and confidence regions (62G15) Nonparametric statistical resampling methods (62G09)

Related Items (18)

Factor-driven two-regime regression ⋮ THE BOOTSTRAP IN THRESHOLD REGRESSION ⋮ Oracle Estimation of a Change Point in High-Dimensional Quantile Regression ⋮ Asymptotics for \(p\)-value based threshold estimation in regression settings ⋮ Bootstrapping a change-point Cox model for survival data ⋮ Bootstrap Adjustment to Minimum p-Value Method for Predictive Classification ⋮ Bootstrap adjusted predictive classification for identification of subgroups with differential treatment effects under generalized linear models ⋮ Self‐Weighted Lad‐Based Inference for Heavy‐Tailed Continuous Threshold Autoregressive Models ⋮ Inference for Local Parameters in Convexity Constrained Models ⋮ Change-point in stochastic design regression and the bootstrap ⋮ A continuous mapping theorem for the smallest argmax functional ⋮ Adaptive estimation of the threshold point in threshold regression ⋮ Robust inference for threshold regression models ⋮ The Lasso for High Dimensional Regression with a Possible Change Point ⋮ Asymptotic confidence sets for the jump curve in bivariate regression problems ⋮ On distinguishing multiple changes in mean and long-range dependence using local Whittle estimation ⋮ A piecewise polynomial trend against long range dependence ⋮ Confidence intervals for multiple isotonic regression and other monotone models

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