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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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