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I Nyoman Budiantara
Departemen Statistika, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
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Pemilihan Bandwidth Optimal pada Regresi Nonparametrik Kernel Menggunakan Metode Unbiased Risk (UBR) Muchni Illahi Efendi; I Nyoman Budiantara; Vita Ratnasari
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.619-630

Abstract

Nonparametric regression is used when the relationship pattern between the response and predictor variables is not clearly defined. Among several nonparametric regression approaches, the kernel method is commonly used. The selection of the optimal bandwidth is a key factor in kernel regression since it significantly affects the estimation accuracy. The optimal bandwidth selection can be obtained using the Unbiased Risk (UBR) method. The study aims to derive the mathematical formulation of the UBR method and evaluate its effectiveness in determining the optimal bandwidth for Indonesia’s 2024 economic growth rate data. The results indicate that the UBR method can be utilized in choosing the optimal bandwidth in nonparametric kernel regression for the economic growth rate data in Indonesia in 2024, producing bandwidths for each predictor of , and , with a minimum UBR of 2.827 and an MSE of 2.198. This implies that the nonparametric regression model with the optimal bandwidth obtained using the UBR method has good predictive capability with a relatively low estimation error for the economic growth rate data in Indonesia.
Co-Authors Muchni Illahi Efendi Vita Ratnasari