The article introduces a new method for estimating bandwidth in nonparametric regression models. By using a Bayesian sampling approach, the researchers approximate the error density with a mixture of Gaussian densities. This method, called the kernel-form error density, improves the accuracy of estimating the regression function compared to previous methods. The researchers found that their proposed bandwidth estimation method outperforms traditional methods like rule-of-thumb and cross-validation in terms of integrated squared errors. This new approach was validated in regression models involving firm ownership concentration and state-price density estimation.