Weight Estimation Using Generalized Moving Average

Jerry D. T. Purnomo, I.N. Budiantara, Kartika Fitriasari


Estimation of regression curve usually conducted using three methods; parametric method, non-parametric method, and semi-parametric method. Non-parametric method has several techniques, which are histogram, kernel, and spline. From various types of spline techniques, weighted parsial spline is developed to solve heterokedasticity problem, this is due to the inability of original partial spline model in handling the heterokedasticity problem. Different techniques are used in choosing the weighted criteria, one of the technique is Generalized Moving Average (GMA). Study about the amount of electricity power loss in PT. PLN East Java Province, North Surabaya Region, resulted that there was a tendency of heterogeneous variance.Using weighted partial spline model with GMA method give better result than original partial spline model. This finding indicates the model of weighted partial spline using GMA method is better than original partial spline model in explaining the heterogeneity of variance.


Weighted Partial Spline; Generalized Moving Average; Original Spline

Full Text:



Dewayani, I. “Penerapan Model Nonparametrik Dengan Metode Spline Pada Jumlah Energi Listrik Yang Hilang di PT PLN Distribusi Jawa Timur Wilayah Surabaya Utara”, Tugas Akhir, ITS, Surabaya. 2004.

He,X. dan Shi,P. “Bivariate Tensor Product B-Spline in a Partly

Linear Models”, Journal of Multivariate Analysis, 58, 162-181.1996.

Engle, R.L, Granger, C., Rice, J. and Weiss, A. “Semiparametric Estimates of Relation Between Weather and Electricity Sales”, Journal of The American Statistical Association, 81, 310-320. 1986.

Green, P., Jennison, C.,Seheult, A. Analysis of Field Experiments by Least Square Smoothing, Journal of The Royal Statistical Society, Ser. B, 47, 299-314. 1985.

Heckman, N. “Spline Smoothing in a Partly Linear Models”,

Journal of The Royal Statistical Society, ser B, 48, 244-248.1986.

Eubank, R.L. A Note on Smoothness Priors and Nonlinear Regression., Journal of the American Statistical Association, 81, 514-517. 1986.

Wahba, G. “Spline Models for Observasional Data”, SIAM, Pensylvania. 1990.

Chen, H. dan Shiau, J.J.H. “Data Driven Efficient Estimators for a Partially Linear Model”. The Annals of Statistics, 22, 211-237.

Shi, P., dan Li, G. On the Rate Convergence of “Minimum L1- Norm”Estimates in a partly Linear Model, Communication in Statistics, Theory and Methods, 23, 175-196. 1994.

Budiantara, I.N. “Estimator Spline Terbobot Dalam Regresi Semiparametrik”, Majalah Ilmu Pengetahuan dan Teknologi, 10, 103-109. 1999.

Subanar dan Budiantara, I.N. “Weighted Spline Estimator in a Partially Linear Models”, Proceeding of the SEAMS-GMU International Conference 1999 on Mathematics and Its Applications, 61-70. 1999.

Montgomery, D.C and Peck, E.A. Introduction to Linear Regression Analysis, New York. ohn Wiley and Sons. 1982.

Silverman, B.W. “Some Aspect of The Spline Smoothing Approach to Nonparametric Regression Curve Fitting (With Discussion)”, Journal of The Royal Statistical Society, ser B , 47,

-52. 1985.

DOI: http://dx.doi.org/10.12962/j20882033.v19i4.140


  • There are currently no refbacks.

Creative Commons License

IPTEK Journal of Science and Technology by Lembaga Penelitian dan Pengabdian kepada Masyarakat, ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at http://iptek.its.ac.id/index.php/jts.