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
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