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Block average quantile regression for massive dataset

Author

Listed:
  • Qifa Xu

    (Hefei University of Technology)

  • Chao Cai

    (Hefei University of Technology)

  • Cuixia Jiang

    (Hefei University of Technology)

  • Fang Sun

    (Queens College)

  • Xue Huang

    (Florida State University)

Abstract

Nowadays, researchers are frequently confronted with challenges from large-scale data computing. Quantile regression on massive dataset is challenging due to the limitations of computer primary memory. Our proposed block average quantile regression provides a simple and efficient way to implement quantile regression on massive dataset. The major novelty of this method is splitting the entire data into a few blocks, applying the convectional quantile regression onto the data within each block, and deriving final results through aggregating these quantile regression results via simple average approach. While our approach can significantly reduce the storage volume needed for estimation, the resulting estimator is theoretically as efficient as the traditional quantile regression on entire dataset. On the statistical side, asymptotic properties of the resulting estimator are investigated. We verify and illustrate our proposed method via extensive Monte Carlo simulation studies as well as a real-world application.

Suggested Citation

  • Qifa Xu & Chao Cai & Cuixia Jiang & Fang Sun & Xue Huang, 2020. "Block average quantile regression for massive dataset," Statistical Papers, Springer, vol. 61(1), pages 141-165, February.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:1:d:10.1007_s00362-017-0932-6
    DOI: 10.1007/s00362-017-0932-6
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    References listed on IDEAS

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    1. Rong Jiang & Wei-Min Qian & Zhan-Gong Zhou, 2016. "Single-index composite quantile regression with heteroscedasticity and general error distributions," Statistical Papers, Springer, vol. 57(1), pages 185-203, March.
    2. Bantli, Faouzi El & Hallin, Marc, 1999. "L1-estimation in linear models with heterogeneous white noise," Statistics & Probability Letters, Elsevier, vol. 45(4), pages 305-315, December.
    3. Okada, Keisuke & Samreth, Sovannroeun, 2012. "The effect of foreign aid on corruption: A quantile regression approach," Economics Letters, Elsevier, vol. 115(2), pages 240-243.
    4. Runze Li & Dennis K.J. Lin & Bing Li, 2013. "Statistical inference in massive data sets," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 29(5), pages 399-409, September.
    5. Hu Yang & Huilan Liu, 2016. "Penalized weighted composite quantile estimators with missing covariates," Statistical Papers, Springer, vol. 57(1), pages 69-88, March.
    6. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    7. David Powell & Joachim Wagner, 2021. "The Exporter Productivity Premium Along the Productivity Distribution: Evidence from Quantile Regression with Nonadditive Firm Fixed Effects," World Scientific Book Chapters, in: Joachim Wagner (ed.), MICROECONOMETRIC STUDIES OF FIRMS’ IMPORTS AND EXPORTS Advanced Methods of Analysis and Evidence from German Enterprises, chapter 9, pages 121-149, World Scientific Publishing Co. Pte. Ltd..
    8. Arcones, Miguel A., 1996. "The Bahadur-Kiefer Representation of Lp Regression Estimators," Econometric Theory, Cambridge University Press, vol. 12(2), pages 257-283, June.
    9. Ning, Zijun & Tang, Linjun, 2014. "Estimation and test procedures for composite quantile regression with covariates missing at random," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 15-25.
    10. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    11. Peng, Limin & Huang, Yijian, 2008. "Survival Analysis With Quantile Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 637-649, June.
    12. Wang, Huixia & He, Xuming, 2007. "Detecting Differential Expressions in GeneChip Microarray Studies: A Quantile Approach," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 104-112, March.
    13. Koenker R. & Geling O., 2001. "Reappraising Medfly Longevity: A Quantile Regression Survival Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 458-468, June.
    14. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    15. Xu, Qifa & Niu, Xufeng & Jiang, Cuixia & Huang, Xue, 2015. "The Phillips curve in the US: A nonlinear quantile regression approach," Economic Modelling, Elsevier, vol. 49(C), pages 186-197.
    16. Rahim Alhamzawi, 2015. "Model selection in quantile regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(2), pages 445-458, February.
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    3. Islam, M.S. & Das, Barun K. & Das, Pronob & Rahaman, Md Habibur, 2021. "Techno-economic optimization of a zero emission energy system for a coastal community in Newfoundland, Canada," Energy, Elsevier, vol. 220(C).

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