Inverse probability weighted estimators for single-index models with missing covariates

Tingting Li and Hu Yang

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 5, 1199-1214

Abstract: In this article, we consider the inverse probability weighted estimators for a single-index model with missing covariates when the selection probabilities are known or unknown. It is shown that the estimator for the index parameter by using estimated selection probabilities has a smaller asymptotic variance than that with true selection probabilities, thus is more efficient. Therefore, the important Horvitz-Thompson property is verified for the index parameter in single index model. However, this difference disappears for the estimators of the link function. Some numerical examples and a real data application are also conducted to illustrate the performances of the estimators.

Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1080/03610926.2012.705208

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