BibTex Citation Data :
@article{J.Gauss16951, author = {Nabila Zia and Suparti Suparti and Diah Safitri}, title = {PEMODELAN REGRESI SPLINE MENGGUNAKAN METODE PENALIZED SPLINE PADA DATA LONGITUDINAL (Studi Kasus: Harga Penutupan Saham LQ45 Sektor Keuangan dengan Kurs USD terhadap Rupiah Periode Januari 2011-Januari 2016)}, journal = {Jurnal Gaussian}, volume = {6}, number = {2}, year = {2017}, keywords = {stock price, USD currency rate, longitudinal data, spline regression, penalized spline}, abstract = { Nonparametric regression is one type of regression analysis used when parametric regression assumptions are not fulfilled. Nonparametric regression is used when the curve does not form a specific pattern of connections. One of the approach by using nonparametric regression is spline regression with penalized spline method. Spline regression using penalized spline method was applied to three closing stock prices on the financial sector such as Bank BRI, BCA and Mandiri with the data of USD currency rate in rupiah. Closing price of stock data and the USD currency rate in rupiah were taken from January 2011 up to January 2016 for in sample data and from February 2016 up to December 2016 for out sample data. The data taken is called longitudinal data which is observing some subjects on specific period. Best spline regression model with penalized spline method is derived from the minimum value of GCV, the number of optimal knots and the optimal orde. Best spline regression model with penalized spline method for longitudinal data was obtained on the orde of 1, the 59 knots, the smoothing parameter with λ value of 1 and the GCV value of 889,797. The R 2 value of in sample data was 99,292%, best model performance for in sample data. MAPE value of out sample data is 1,057%, the best accurate performance model. Keyword: stock price, USD currency rate, longitudinal data, spline regression, penalized spline }, issn = {2339-2541}, pages = {221--230} doi = {10.14710/j.gauss.6.2.221-230}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/16951} }
Refworks Citation Data :
Nonparametric regression is one type of regression analysis used when parametric regression assumptions are not fulfilled. Nonparametric regression is used when the curve does not form a specific pattern of connections. One of the approach by using nonparametric regression is spline regression with penalized spline method. Spline regression using penalized spline method was applied to three closing stock prices on the financial sector such as Bank BRI, BCA and Mandiri with the data of USD currency rate in rupiah. Closing price of stock data and the USD currency rate in rupiah were taken from January 2011 up to January 2016 for in sample data and from February 2016 up to December 2016 for out sample data. The data taken is called longitudinal data which is observing some subjects on specific period. Best spline regression model with penalized spline method is derived from the minimum value of GCV, the number of optimal knots and the optimal orde. Best spline regression model with penalized spline method for longitudinal data was obtained on the orde of 1, the 59 knots, the smoothing parameter with λ value of 1 and the GCV value of 889,797. The R2 value of in sample data was 99,292%, best model performance for in sample data. MAPE value of out sample data is 1,057%, the best accurate performance model.
Keyword: stock price, USD currency rate, longitudinal data, spline regression, penalized spline
Article Metrics:
Last update:
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Media Statistika journal and Department of Statistics, Universitas Diponegoro as the publisher of the journal. Copyright encompasses the rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
Jurnal Gaussian and Department of Statistics, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in Jurnal Gaussian journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form Jurnal Gaussian]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Dr. Rukun Santoso (Editor-in-Chief) Editorial Office of Jurnal GaussianDepartment of Statistics, Universitas DiponegoroJl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275Telp./Fax: +62-24-7474754Email: jurnalgaussian@gmail.com
Jurnal Gaussian by Departemen Statistika Undip is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Visitor Number:
View statistics
