BibTex Citation Data :
@article{Medstat2479, author = {Hasbi Yasin}, title = {ESTIMASI REGRESI NON PARAMETRIK DENGAN METODE WAVELET SHRINKAGE NEURAL NETWORK PADA MODEL RANCANGAN TETAP}, journal = {MEDIA STATISTIKA}, volume = {2}, number = {1}, year = {2009}, keywords = {}, abstract = { If X is a predictor variable and Y is a response variable of following model Y = g(X) +e with function g is a regression which not yet been known and e is an independent random variable with mean 0 and variant . The function of g can be estimated by parametric and nonparametric approach. In this paper, g is estimated by nonparametric approach that is named wavelet shrinkage neural network method. At this method, the smoothly function estimation is depending on shrinkage parameter’s that are threshold value and level of wavelet that be used. It also depending on the number of neuron in the hidden layer and the number of epoch that be used in feed forward neural network. Therefore, it is required to be select the optimal value of threshold, level of wavelet, the number of neuron and the number of epoch to determine optimal function estimation. Keywords : Nonparametric Regression, Wavelet Shrinkage Neural Network }, issn = {2477-0647}, pages = {1--10} doi = {10.14710/medstat.2.1.1-10}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2479} }
Refworks Citation Data :
If X is a predictor variable and Y is a response variable of following model Y = g(X) +e with function g is a regression which not yet been known and e is an independent random variable with mean 0 and variant . The function of g can be estimated by parametric and nonparametric approach. In this paper, g is estimated by nonparametric approach that is named wavelet shrinkage neural network method. At this method, the smoothly function estimation is depending on shrinkage parameter’s that are threshold value and level of wavelet that be used. It also depending on the number of neuron in the hidden layer and the number of epoch that be used in feed forward neural network. Therefore, it is required to be select the optimal value of threshold, level of wavelet, the number of neuron and the number of epoch to determine optimal function estimation.
Keywords: Nonparametric Regression, Wavelet Shrinkage Neural Network
Article Metrics:
Last update:
Last update: 2024-11-24 05:31:54
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.
Media Statistika journal 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 Media Statistika journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form Media Statistika]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Dr. Di Asih I Maruddani (Editor-in-Chief) Editorial Office of Media StatistikaDepartment of Statistics, Universitas DiponegoroJl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275Telp./Fax: +62-24-7474754Email: maruddani@live.undip.ac.id
Media Statistika
Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro
Gedung F Lantai 3, Jalan Prof Jacub Rais, Kampus Tembalang
Semarang 50275
Indexing:
