An Extended Single Index Model with Missing Response at Random
An extended single-index model is considered when responses are missing at random. A three-step estimation procedure is developed to define an estimator for the single index parameter vector by a joint estimating equation. The proposed estimator is shown to be asymptotically normal. An iterative scheme for computing this estimator is proposed. This algorithm only involves one-dimensional nonparametric smoothers, thereby avoiding the data sparsity problem caused by high model dimensionality. Some simulation study is conducted to investigate the finite sample performances of the pro- posed estimators.
|Date of creation:||Jan 2014|
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Web page: http://sfb649.wiwi.hu-berlin.de
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- Wang Q. & Linton O. & Hardle W., 2004.
"Semiparametric Regression Analysis With Missing Response at Random,"
Journal of the American Statistical Association,
American Statistical Association, vol. 99, pages 334-345, January.
- Wolfgang Härdle & Oliver Linton & Wang, Qihua, 2003. "Semiparametric regression analysis with missing response at random," CeMMAP working papers CWP11/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Ding, Xiaobo & Wang, Qihua, 2011. "Fusion-Refinement Procedure for Dimension Reduction With Missing Response at Random," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1193-1207.
- Zonghui Hu & Dean A. Follmann & Jing Qin, 2010. "Semiparametric dimension reduction estimation for mean response with missing data," Biometrika, Biometrika Trust, vol. 97(2), pages 305-319. Full references (including those not matched with items on IDEAS)
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