Computing confidence intervals from massive data via penalized quantile smoothing splines

DOI10.1016/j.csda.2019.106885zbMath1504.62064OpenAlexW2990955907MaRDI QIDQ2291323

Andrew J. Berglund, Nirmal Govind, Likun Zhang, Enrique Del Castillo, Martin P. Tingley

Publication date: 30 January 2020

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2019.106885

zbMATH Keywords

cross-validationpenalized splinesA/B testingbag of little bootstrapsquantile smoothing

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric tolerance and confidence regions (62G15) Statistical aspects of big data and data science (62R07)

Uses Software

• TRIPACK
• quantreg
• fda (R)

Cites Work

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• A Scalable Bootstrap for Massive Data

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