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http://dspace.vgtu.lt/handle/1/3634
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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. |
| URI: | http://dspace.vgtu.lt/handle/1/3634 |
| ISSN: | 1392-5113 |
| Appears in Collections: | Moksliniai straipsniai / Research articles
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