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Submitted
October 11, 2020
Accepted
December 25, 2020
Published
December 30, 2020
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Abstract

This study aims to model Crude Birth Rates (CBR) in West Nusa Tenggara Province. The nonparametric regression method was used in this research by considering data distribution patterns that do not show a linear relationship between variables. In this case, the kernel nonparametric regression using the Gaussian function and the Nadaraya-Watson estimator. The results showed optimal bandwidths of 0.55542837, 1.29042927, 0.94706041, and 0.92278896 with a value of minimum Generalized Cross-Validation (GCV) of 0.000000000432613511, which was minimized by the simulated annealing algorithm. The resulting model's accuracy can be seen from the coefficient of determination (R2) of 99.23% and the Mean Absolute Percentage Error (MAPE) of 0.007049%.

Keywords

bandwidth Generalized Cross-Validation MAPE Nadaraya-Watson estimator simulated annealing algorithm

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All articles published in the Eigen Mathematics Journal will be available for free reading and downloading. The license applied to this journal is Creative Commons Attribution-Non-Commercial-Share Alike (CC BY-NC-SA).

How to Cite
Pratiwi, D., Mursy, L. A. A., Rizaldi, M., & Fitriyani, N. (2020). Regresi Nonparametrik Kernel Gaussian pada Pemodelan Angka Kelahiran Kasar di Provinsi Nusa Tenggara Barat. EIGEN MATHEMATICS JOURNAL, 3(2), 100–105. https://doi.org/10.29303/emj.v3i2.78
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References

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