A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation
多元核回归带宽选择的贝叶斯方法及其状态价格密度应用
This paper presents a Bayesian approach to bandwidth selection for multivariate kernel regression. A Monte Carlo study shows that under the average squared error criterion, the Bayesian bandwidth selector is comparable to the cross-validation method and clearly outperforms the bootstrapping and rule-of-thumb bandwidth selectors. The Bayesian bandwidth selector is applied to a multivariate kernel regression model that is often used to estimate the state-price density of…