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General matching quantiles M-estimation

Author

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  • Qin, Shanshan
  • Wu, Yuehua

Abstract

Matching quantiles estimation (MQE) is a useful technique that allows one to find a linear combination of a set of random variables that matches the distribution of a target random variable. Since it is based on ordinary least-squares (OLS), it may be sensitive to outlier observations of the target random variable. A general matching quantiles M-estimation (MQME) method is thus proposed, which is resistant to outlier observations of the target random variable. Given that in most applications, the number of variables p may be large, a ‘sparse’ representation is highly desirable. The MQME is combined with the adaptive Lasso penalty so it can select informative variables. An iterative algorithm based on M-estimation is developed to compute MQME. The proposed matching quantiles M-estimate is consistent, just like the MQE. Extensive simulations are provided, in which efficient finite-sample performance of the new method is demonstrated. In addition, an illustrative real case study is presented.

Suggested Citation

  • Qin, Shanshan & Wu, Yuehua, 2020. "General matching quantiles M-estimation," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
    • Handle: RePEc:eee:csdana:v:147:y:2020:i:c:s0167947320300323
    DOI: 10.1016/j.csda.2020.106941
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    References listed on IDEAS

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    1. Roshan Srivastav & Andre Schardong & Slobodan Simonovic, 2014. "Equidistance Quantile Matching Method for Updating IDFCurves under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2539-2562, July.
    2. repec:ulb:ulbeco:2013/136280 is not listed on IDEAS
    3. Jianqing Fan & Quefeng Li & Yuyan Wang, 2017. "Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 247-265, January.
    4. Nikolaos Sgouropoulos & Qiwei Yao & Claudia Yastremiz, 2015. "Matching a Distribution by Matching Quantiles Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 742-759, June.
    5. 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.
    6. Dominicy, Yves & Veredas, David, 2013. "The method of simulated quantiles," Journal of Econometrics, Elsevier, vol. 172(2), pages 235-247.
    7. Cantoni E. & Ronchetti E., 2001. "Robust Inference for Generalized Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1022-1030, September.
    8. Sgouropoulos, Nikolaos & Yao, Qiwei & Yastremiz, Claudia, 2015. "Matching a distribution by matching quantiles estimation," LSE Research Online Documents on Economics 57221, London School of Economics and Political Science, LSE Library.
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