Optimal subsampling for composite quantile regression in big data

Yuan, Xiaohui; Li, Yong; Dong, Xiaogang; Liu, Tianqing

doi:10.1007/s00362-022-01292-1

Cited by 10 publications

(2 citation statements)

References 25 publications

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“…In this paper, we only consider the subsampling for the scalar-on-function quantile regression at the single quantile level. As done in Shao and Wang (2021); Yuan et al (2022), it is interesting to investigate multiple quantile level.…”

Section: Discussionmentioning

confidence: 99%

“…Later, Yao and Wang (2019) and Ai et al (2021b) extended the subsampling method to softmax regression and generalized linear models, respectively. Very recently, Wang and Ma (2021), Ai et al (2021a), Fan et al (2021), and Shao et al (2022) employed the optimal subsampling method to ordinary quantile regression, and Shao and Wang (2021) and Yuan et al (2022) developed the subsampling for composite quantile regression.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal subsampling for functional quantile regression

Yan¹,

Li²,

Niu³

2022

Preprint

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Subsampling is an efficient method to deal with massive data. In this paper, we investigate the optimal subsampling for linear quantile regression when the covariates are functions. The asymptotic distribution of the subsampling estimator is first derived. Then, we obtain the optimal subsampling probabilities based on the A-optimality criterion. Furthermore, the modified subsampling probabilities without estimating the densities of the response variables given the covariates are also proposed, which are easier to implement in practise. Numerical experiments on synthetic and real data show that the proposed methods always outperform the one with uniform sampling and can approximate the results based on full data well with less computational efforts.

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Section: Discussionmentioning

confidence: 99%