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http://dx.doi.org/10.5351/KJAS.2018.31.3.343

Comparison of estimation methods for expectile regression  

Kim, Jong Min (Department of Statistics, Hankuk University of Foreign Studies)
Kang, Kee-Hoon (Department of Statistics, Hankuk University of Foreign Studies)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.3, 2018 , pp. 343-352 More about this Journal
Abstract
We can use quantile regression and expectile regression analysis to estimate trends in extreme regions as well as the average trends of response variables in given explanatory variables. In this paper, we compare the performance between the parametric and nonparametric methods for expectile regression. We introduce each estimation method and analyze through various simulations and the application to real data. The nonparametric model showed better results if the model is complex and difficult to deduce the relationship between variables. The use of nonparametric methods can be recommended in terms of the difficulty of assuming a parametric model in expectile regression.
Keywords
cross-validation; nonparametric method; parametric method; P-spline; quantile regression;
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