The Nonparametric Kernel Method using NadarayaWatson, Priestley-Chao and Gasser-Muller Estimators for
the Estimation of the Rainfall Data in Lampung

Herawati. N; Sayuti. S.F; Nisa. K; Setiawan. E

doi:https://doi.org/10.14445/22315373/IJMTT-V68I8P502

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 68 | Issue 8 | Year 2022 | Article Id. IJMTT-V68I8P502 | DOI : https://doi.org/10.14445/22315373/IJMTT-V68I8P502

The Nonparametric Kernel Method using NadarayaWatson, Priestley-Chao and Gasser-Muller Estimators for the Estimation of the Rainfall Data in Lampung

Herawati. N, Sayuti. S.F, Nisa. K, Setiawan. E

Received	Revised	Accepted	Published
21 Jun 2022	22 Jul 2022	02 Aug 2022	15 Aug 2022

Abstract

There are two methods used in regression analysis, namely parametric and nonparametric. Parametric regression, assumes many assumptions, such as the normality of data, the distribution of data that forms a certain pattern, and others. In nonparametric regression, these assumptions do not have to be fulfilled. To estimate the nonparametric regression function, smoothing techniques are needed, one of which is the kernel method. In this study, the nonparametric regression estimators of Nadaraya-Watson, Priestley & Chao and Gasser & Müller were compared based on the smallest GCV value and the best kernel estimator was determined by the smallest error rate in the rainfall data in the province of Lampung. The results showed that the Nadaraya-Watson estimator was determined as the best estimator with a bandwidth (h) of 5.564 and had the smallest error value compared to the other two methods, namely MSE=5.9773 and MAPE=0.1935.

Keywords

Kernel estimator, Bandwidth, Nadaraya-Watson, Priestley-Chao, Gasser-Muller, MSE, MAPE.

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Citation :

Herawati. N, Sayuti. S.F, Nisa. K, Setiawan. E, "The Nonparametric Kernel Method using NadarayaWatson, Priestley-Chao and Gasser-Muller Estimators for the Estimation of the Rainfall Data in Lampung," International Journal of Mathematics Trends and Technology (IJMTT), vol. 68, no. 8, pp. 12-20, 2022. Crossref, https://doi.org/10.14445/22315373/IJMTT-V68I8P502