Estimasi Fungsi Regresi Dalam Model Regresi Nonparametrik Birespon Menggunakan Estimator Smoothing Spline dan Estimator Kernel
DOI:
https://doi.org/10.20956/jmsk.v15i2.5710Keywords:
kernel estimator, smoothing spline estimator, regression function, bi-respond nonparametric regression modelAbstract
Abstract
Regression model of bi-respond nonparametric is a regression model which is illustrating of the connection pattern between respond variable and one or more predictor variables, where between first respond and second respond have correlation each other. In this paper, we discuss the estimating functions of regression in regression model of bi-respond nonparametric by using different two estimation techniques, namely, smoothing spline and kernel. This study showed that for using smoothing spline and kernel, the estimator function of regression which has been obtained in observation is a regression linier. In addition, both estimators that are obtained from those two techniques are systematically only different on smoothing matrices.
Keywords: kernel estimator, smoothing spline estimator, regression function, bi-respond nonparametric regression model.
Abstrak
Model regresi nonparametrik birespon adalah suatu model regresi yang menggambarkan pola hubungan antara dua variabel respon dan satu atau beberapa variabel prediktor dimana antara respon pertama dan respon kedua berkorelasi. Dalam makalah ini dibahas estimasi fungsi regresi dalam model regresi nonparametrik birespon menggunakan dua teknik estimasi yang berbeda, yaitu smoothing spline dan kernel. Hasil studi ini menunjukkan bahwa, baik menggunakan smoothing spline maupun menggunakan kernel, estimator fungsi regresi yang didapatkan merupakan fungsi linier dalam observasi. Selain itu, kedua estimator fungsi regresi yang didapatkan dari kedua teknik estimasi tersebut secara matematis hanya dibedakan oleh matriks penghalusnya.
Kata Kunci : Estimator Kernel, Estimator Smoothing Spline, Fungsi Regresi, Model Regresi Nonparametrik Birespon.
References
. Aydin, D., 2007. A comparison of the nonparametric regression models using smoothing spline and kernel regression. World Acad. Sci. Eng. Technol., 26, 730-734.
. Chamidah, N. dan Lestari,B., 2016. Spline Estimator in Homoscedastic Multi-Response Nonparametric Regression Model in Case of Unbalanced Number of Observations. Far East Journal of Mathematical Sciences (FJMS), 100(9), 1433-1453.
. Eubank, R.L., 1999. Nonparametric Regression and Smoothing Spline, Marcel Deker, New York.
. Green, P.J. dan Silverman, B.W. 1994. Nonparametric Regression and Generalized Linear Models. Chapman Hall. New York.
. Hardle, W., 1991. Applied Nonparametric Regression. Cambridge University Press. Cambridge.
. Lestari, B., Budiantara, I.N., Sunaryo, S., dan Mashuri, M., 2012. Spline smoothing for multi-response nonparametric regression model in case of heteroscedasticity of variance. J. Math. and Stat. 8, 377-384.
. Lestari, B., Fatmawati, dan Budiantara, I. N., 2017. Estimasi Fungsi Regresi Nonparametrik Multirespon Menggunakan Reproducing Kernel Hilbert Space Berdasarkan Estimator Smoothing Spline. Proceeding of National Seminar on Mathematics and Its Applications (SNMA) 2017, Airlangga University, Surabaya, pp. 243-250.
. Nadaraya, E. A., 1964. On Estimating Regression. Theory Prob. Applications, 10, 186-190.
. Schimek, M. G., 2000. Smoothing and Regression. John Wiley & Sons, New York.
. Wand, M. P. dan Jones, M.C., 1995. Kernel Smoothing. Chapman Hall, New York.
. Wang, Y., W. Guo dan Brown, W.B., 2000. Spline smoothing for bivariate data with applications to association between hormones. Staistica Sinica. 10, 377-397.
. Watson, G. S., 1964. Smooth Regression Analysis. Sankhya Series A. 26, 359-372.
. Yatcchew, A., 2003. Semiparametric Regression for the Applied Econometrician. Cambridge University Press, Cambridge.
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