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|Title:||Optimal zone for bandwidth selection in semiparametric models||Authors:||Li, J.
|Keywords:||Asymptotic mean square error
Neumann series approximation
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|
|Appears in Collections:||Staff Publications|
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