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

  IJMTT-book-cover
 
International Journal of Mathematics Trends and Technology (IJMTT)
 
© 2022 by IJMTT Journal
Volume-68 Issue-8
Year of Publication : 2022
Authors : Herawati. N, Sayuti. S.F, Nisa. K, Setiawan. E
 10.14445/22315373/IJMTT-V68I8P502

How to Cite?

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

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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