Optimal zone for bandwidth selection in semiparametric models
We study the general problem of bandwidth selection in semiparametric regression. By expanding the higher-order terms in the Taylor series for the asymptotic mean-squared error, we provide a theoretical justification for the earlier empirical observations of an optimal zone of bandwidths in the literature. Based on the idea of cross-validating parametrical estimates, we further introduce a novel bandwidth selector for semiparametric models. The method is demonstrated by numerical studies to be able to preserve the selected bandwidth within the optimal zone. This data-driven cross-validation method may also be applicable for model diagnosis and longitudinal data settings. Examples from two clinical trials are provided to illustrate the applications.
optimal bandwidth, cross-validation, asymptotic mean square error, Taylor series expansion, Neumann series approximation
Statistical Theory | Statistics and Probability
Journal of Nonparametric Statistics
American Statistical Association
LI, Jialiang; ZHANG, Wenyang; and WU, Zhengxiao.
Optimal zone for bandwidth selection in semiparametric models. (2011). Journal of Nonparametric Statistics. 23, (3), 701-717. Research Collection School of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research_all/11