Adaptive quantile regressions for massive datasets

Rong Jiang (), Wei-wei Chen () and Xin Liu ()
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Rong Jiang: Donghua University
Wei-wei Chen: Donghua University
Xin Liu: Donghua University

Statistical Papers, 2021, vol. 62, issue 4, No 17, 1995 pages

Abstract: Abstract Analysis of massive datasets is challenging owing to limitations of computer primary memory. Adaptive quantile regressions is a robust and efficient estimation method. For computational efficiency, we propose an adaptive smoothing quantile regressions (ASQR). The ASQR method is used to analyze massive datasets. The proposed approach significantly reduces the required amount of primary memory, and the resulting estimate will be as efficient as if the entire data set is analyzed simultaneously. Both simulations and data analysis are conducted to illustrate the finite sample performance of the proposed methods.

Keywords: Massive dataset; Divide and conquer; Adaptive quantile regressions; Smoothing method (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00362-020-01170-8

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