I Nyoman Budiantara


We considered the nonparametric regression model : Zj = X(tj) + ej, j = 1,2,,n, where X(tj) is the regression curve. The random error ej are independently distributed normal with a zero mean and a variance s2/bj, bj > 0. The estimation of X obtained by minimizing a Weighted Least Square. The solution of this optimation is a Weighted Spline Polynomial. Further, we give an application of weigted spline estimator in nonparametric regression.

Abstract in Bahasa Indonesia :

Diberikan model regresi nonparametrik : Zj = X(tj) + ej, j = 1,2,,n, dengan X (tj) kurva regresi dan ej sesatan random yang diasumsikan berdistribusi normal dengan mean nol dan variansi s2/bj, bj > 0. Estimasi kurva regresi X yang meminimumkan suatu Penalized Least Square Terbobot, merupakan estimator Polinomial Spline Natural Terbobot. Selanjutnya diberikan suatu aplikasi estimator spline terbobot dalam regresi nonparametrik.

Kata kunci: Spline terbobot, Regresi nonparametrik, Penalized Least Square.


Weighted Spline, Nonparametric Regression, Penalized Least Square.

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The Journal is published by The Institute of Research & Community Outreach - Petra Christian University. It available online supported by Directorate General of Higher Education - Ministry of National Education - Republic of Indonesia.

©All right reserved 2016.Jurnal Teknik Industri, ISSN: 1411-2485, e-ISSN: 2087-7439

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