Kernel‐based Generalized Cross‐validation in Non‐parametric Mixed‐effect Models
Wangli Xu and
Lixing Zhu
Scandinavian Journal of Statistics, 2009, vol. 36, issue 2, 229-247
Abstract:
Abstract. Although generalized cross‐validation (GCV) has been frequently applied to select bandwidth when kernel methods are used to estimate non‐parametric mixed‐effect models in which non‐parametric mean functions are used to model covariate effects, and additive random effects are applied to account for overdispersion and correlation, the optimality of the GCV has not yet been explored. In this article, we construct a kernel estimator of the non‐parametric mean function. An equivalence between the kernel estimator and a weighted least square type estimator is provided, and the optimality of the GCV‐based bandwidth is investigated. The theoretical derivations also show that kernel‐based and spline‐based GCV give very similar asymptotic results. This provides us with a solid base to use kernel estimation for mixed‐effect models. Simulation studies are undertaken to investigate the empirical performance of the GCV. A real data example is analysed for illustration.
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
https://doi.org/10.1111/j.1467-9469.2008.00625.x
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:36:y:2009:i:2:p:229-247
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0303-6898
Access Statistics for this article
Scandinavian Journal of Statistics is currently edited by ÿrnulf Borgan and Bo Lindqvist
More articles in Scandinavian Journal of Statistics from Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association, Swedish Statistical Association
Bibliographic data for series maintained by Wiley Content Delivery ().