Regresi Nonparametrik Kernel Gaussian pada Pemodelan Angka Kelahiran Kasar di Provinsi Nusa Tenggara Barat

Deni Pratiwi, Lalu Abd Azis Mursy, Muhammad Rizaldi, Nurul Fitriyani

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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References


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DOI: https://doi.org/10.29303/emj.v3i2.78

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