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A data analysis of natural gas prices using penalized median regression splines
Journal of the Korean Data & Information Science Society 2022;33:1125-40
Published online November 30, 2022;  https://doi.org/10.7465/jkdi.2022.33.6.1125
© 2022 Korean Data and Information Science Society.

Eun-Ji Lee1 · Ariunjargal Byambaa2 · Jae-Hwan Jhong3

123Department of Information Statistics, Chungbuk National University
Correspondence to: This research was supported by Chungbuk National University Korea National University Development Project (2021).
1 Graduate student of master degree, Department of Information Statistics, Chungbuk National University, Cheongju 28644, Chungbuk, Korea.
2 Graduate student of master degree, Department of Information Statistics, Chungbuk National University, Cheongju 28644, Chungbuk, Korea.
3 Assistant professor, Department of Information Statistics, Chungbuk National University, Cheongju 28644, Chungbuk, Korea. E-mail: jjh25@cbnu.ac.kr
Received October 11, 2022; Revised November 4, 2022; Accepted November 11, 2022.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
We carry out a study on a natural gas prices data analysis based on a penalized median regression spline estimator with B-spline and total variation penalty. Once we express the estimator by a linear combination of a linear B-splines, the coefficients are estimated by minimizing a penalized check loss function. A coordinate descent algorithm is introduced to handle the special penalty determined by the B-spline coefficients. In implementation, we adopt the form of an univariate weighted absolute function, which is a convex and piecewise linear function, and find its slope to find the minimum of each coordinate. We also illustrate the performance of the proposed method using some numerical studies and data analysis. The performance of the proposed estimator is studied through both numerical simulations and the natural gas prices data sets.
Keywords : Coordinate descent algorithm, median regression, natural gas prices, nonparametric function estimation, total variation.