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

Year of publication:	2017
Authors:	Wang, Qihua ; Zhang, Tao ; Härdle, Wolfgang
Publisher:	[S.l.] : SSRN

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