City Research Online

Abstract

We introduce one-sided cross-validation to nonparametric kernel density estimation. The method is more stable than classical cross-validation and it has a better overall performance comparable to what we see in plug-in methods. One-sided cross-validation is a more direct date driven method than plugin methods with weaker assumptions of smoothness since it does not require a smooth pilot with consistent second derivatives. Our conclusions for one-sided kernel density cross-validation are similar to the conclusions obtained by Hart and Li (1998) when they introduced one-sided cross-validation in the regression context. An extensive simulation study conms that our one-sided cross-validation clearly outperforms the simple cross validation. We conclude with real data applications.

Publication Type:	Book Section
Publisher Keywords:	bandwidth choice, cross-validation, plug-in, nonparametric estimation 1This
Subjects:	H Social Sciences > HB Economic Theory
Departments:	Bayes Business School > Actuarial Science & Insurance

Export

Downloads