Optimal subsampling design for polynomial regression in one covariate

Author:

Reuter TorstenORCID,Schwabe Rainer

Abstract

AbstractImprovements in technology lead to increasing availability of large data sets which makes the need for data reduction and informative subsamples ever more important. In this paper we construct D-optimal subsampling designs for polynomial regression in one covariate for invariant distributions of the covariate. We study quadratic regression more closely for specific distributions. In particular we make statements on the shape of the resulting optimal subsampling designs and the effect of the subsample size on the design. To illustrate the advantage of the optimal subsampling designs we examine the efficiency of uniform random subsampling.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Springer Science and Business Media LLC

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference19 articles.

1. Dereziński M, Warmuth MK (2018) Reverse iterative volume sampling for linear regression. J Mach Learn Res 190(1):853–891

2. Drineas P, Mahoney MW, Muthukrishnan S (2006) Sampling algorithms for $$\ell _2$$ regression and applications. In: Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm, pp 1127–1136

3. Fedorov VV (1989) Optimal design with bounded density: optimization algorithms of the exchange type. J Stat Plan Inference 220(1):1–13

4. Gaffke N, Heiligers B (1996) Approximate designs for polynomial regression: invariance, admissibility, and optimality. In: Ghosh S, Rao CR (eds) Handbook of statistics, vol 13. Elsevier, Amsterdam, pp 1149–1199

5. Hasselman B (2018) nleqslv: solve systems of nonlinear equations. R package version 3.3.2. https://CRAN.R-project.org/package=nleqslv

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3