Please use this identifier to cite or link to this item: https://doi.org/10.1080/10485252.2010.533768
Title: Optimal zone for bandwidth selection in semiparametric models
Authors: Li, J. 
Zhang, W.
Wu, Z.
Keywords: Asymptotic mean square error
Cross-validation
Neumann series approximation
Optimal bandwidth
Taylor series expansion
Issue Date: Sep-2011
Citation: Li, J., Zhang, W., Wu, Z. (2011-09). Optimal zone for bandwidth selection in semiparametric models. Journal of Nonparametric Statistics 23 (3) : 701-717. ScholarBank@NUS Repository. https://doi.org/10.1080/10485252.2010.533768
Abstract: 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. © American Statistical Association and Taylor & Francis 2011.
Source Title: Journal of Nonparametric Statistics
URI: http://scholarbank.nus.edu.sg/handle/10635/105288
ISSN: 10485252
DOI: 10.1080/10485252.2010.533768
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