 General matching quantiles M-estimationShanshan Qin and Yuehua Wu Computational Statistics & Data Analysis, 2020, vol. 147, issue C 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. Keywords: Adaptive Lasso; Cross-validation; Matching quantiles; M-estimation; Outlier observations (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:147:y:2020:i:c:s0167947320300323 DOI: 10.1016/j.csda.2020.106941 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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