Estimator Nadaraya-Watson dengan Pendekatan Cross Validation dan Generalized Cross Validation untuk Mengestimasi Produksi Jagung

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>

Parameter Estimation and Hypothesis Testing of Geographically and Temporally Weighted Bivariate Negative Binomial Regression

“…The accuracy of parameter estimation depends on the bandwidth selection, the more optimum the more accurate [19]. Methods to determine optimal bandwidth are (Cross Validation) and GCV (Generalized Cross Validation) [20]. GCV is preferred because it is quite easy to work with and provides better precision in parameter estimation [19,20].…”

“…Methods to determine optimal bandwidth are (Cross Validation) and GCV (Generalized Cross Validation) [20]. GCV is preferred because it is quite easy to work with and provides better precision in parameter estimation [19,20]. The GCV method is defined as follows:…”