Abstract
<p>Nadaraya-Watson Estimator with kernel approach depends on two-parameter, those are kernel function and bandwidth choice. However, between the two of them, bandwidth choice gave a huge impact on the result of the estimation. By minimizing the value of Mean Square Error (MSE), Cross-Validation (CV) and Generalized Cross-Validation (GCV) gave the optimal bandwidth value. In this research, corn production was considered as the dependent variable, while the planted area, harvested area, and the fertilizer as the independent variable. The result of this research showed that Nadaraya-Watson Estimator with Generalized Cross-Validation gives a better corn production estimation with optimal bandwidth value 742392,2, with and with MSE 202583,9.</p><p><strong>Keywords</strong>: kernel, estimator Nadaraya-Watson, cross validation, generalized cross validation.</p>
Highlights
Regresi nonparametrik merupakan metode pendugaan model yang dilakukan berdasarkan pendekatan yang tidak terikat asumsi bentuk kurva regresi tertentu, dimana kurva regresi hanya diasumsikan mulus [1]
Nadaraya-Watson Estimator with kernel approach depends on twoparameter
of them, bandwidth choice gave a huge impact on the result of the estimation
Summary
Salah satu metode untuk menentukan nilai bandwidth adalah Cross Validation (CV). Metode Cross Validation atau sering disebut CV adalah metode pendugaan data untuk menunjukkan apa yang harus dilakukan jika pengulangan observasi tersedia. Pada pemilihan bandwidth yang optimum didasarkan nilai CV yang minimum [9]. Metode Generalized Cross Validation (GCV) dalam regresi kernel adalah satu metode untuk memilih bandwidth optimal dengan meminimalkan fungsi GCV. Optimasi GCV adalah memilih h optimal yang meminimalkan nilai GCV [7]:. Estimator ini untuk memperkirakan m sebagai rata-rata tertimbang secara lokal dengan menggunakan kernel sebagai fungsi pembobotan [15]
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