Nonparametric Estimation of the Conditional Distribution at Regression Boundary Points

DOI10.1080/00031305.2018.1558109OpenAlexW2608357550WikidataQ128205743 ScholiaQ128205743MaRDI QIDQ5869284

Srinjoy Das, Dimitris N. Politis

Publication date: 28 September 2022

Published in: The American Statistician (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1704.00674

zbMATH Keywords

local polynomial fittingkernel smoothinglocal linear regressionmodel-free predictionpoint prediction

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

Model-free bootstrap for a general class of stationary time series ⋮ Uniform convergence results for the local linear regression estimation of the conditional distribution ⋮ Learning sparse conditional distribution: an efficient kernel-based approach ⋮ Predictive Inference for Locally Stationary Time Series With an Application to Climate Data

Uses Software

• KernSmooth
• ISLR

Cites Work

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• A simple nonparametric estimator of a strictly monotone regression function
• Model-free prediction and regression. A transformation-based approach to inference
• Nonparametric kernel regression subject to monotonicity constraints
• Model-free model-fitting and predictive distributions
• Kernel smoothers: an overview of curve estimators for the first graduate course in nonparametric statistics
• Maximum Likelihood Estimates of Monotone Parameters
• Local Linear Quantile Regression
• Methods for Estimating a Conditional Distribution Function
• An Introduction to Statistical Learning

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