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Single-index composite quantile regression for massive data
Journal of Multivariate Analysis ( IF 1.6 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.jmva.2020.104669
Rong Jiang , Keming Yu

Composite quantile regression (CQR) is becoming increasingly popular due to its robustness from quantile regression. Recently, the CQR method has been studied extensively with single-index models. However, the numerical inference of CQR methods for single-index models must involve iteration. In this study, we propose a non-iterative CQR (NICQR) estimation algorithm and derive the asymptotic distribution of the proposed estimator. Moreover, we extend the NICQR method to the analysis of massive datasets via a divide-and-conquer strategy. The proposed approach significantly reduces the computing time and the required primary memory. Simulation studies and two real data applications are conducted to illustrate the finite sample performance of the proposed methods.

中文翻译:

海量数据的单指标复合分位数回归

由于分位数回归的稳健性,复合分位数回归 (CQR) 正变得越来越流行。最近,CQR 方法已被广泛研究与单索引模型。然而,单指标模型的 CQR 方法的数值推理必须涉及迭代。在这项研究中,我们提出了一种非迭代 CQR (NICQR) 估计算法,并推导出了所提出的估计量的渐近分布。此外,我们通过分而治之的策略将 NICQR 方法扩展到对海量数据集的分析。所提出的方法显着减少了计算时间和所需的主存储器。进行了模拟研究和两个实际数据应用,以说明所提出方法的有限样本性能。
更新日期:2020-11-01
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