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Title: Prediction of composite indicators using locally weighted quantile regression
Authors: Rukšėnaitė, Jurga
Vaitkus, Pranas
Asijavičius, Povilas
Keywords: quantile regression
penalty function
extreme learning machine
locally weighted regression
composite indicators
Issue Date: 2018
Publisher: Vilnius University Institute of Mathematics and Informatics
Citation: Rukšėnaitė, J.; Vaitkus, P.; Asijavičius, P. 2018. Prediction of composite indicators using locally weighted quantile regression, Nonlinear analysis: modelling and control 23(1): 19-30
Series/Report no.: 23;1
Abstract: The main goal of this paper is to improve the existing methods and tools used for solving penalized quantile regression problems. We modified the quantile regression method by implementing the extreme learning machine (ELM) algorithm and features of locally weighted regression. Also, we used different penalty functions. A modified method was used for the onestep- ahead prediction of the composite indicator (CI) of the Lithuanian economy. Our analysis showed that the prediction error of the modified locally weighted quantile regression is smaller in comparison to the other quantile regression.
ISSN: 1392-5113
Appears in Collections:Moksliniai straipsniai / Research articles

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