Estimation for a partial-linear single-index model
By Jane-ling Wang, Liugen Xue, Lixing Zhu, Y. S. Chong. 2009
207 citations recorded by the source corpus.
In this paper, we study the estimation for a partial-linear single-index model. A two-stage estimation procedure is proposed to estimate the link function for the single index and the parameters in the single index, as well as the parameters in the linear component of the model. Asymptotic normality is established for both parametric components. For the index, a constrained estimating equation leads to an asymptotically more efficient estimator than existing estimators in the sense that it is of a smaller limiting variance. The estimator of the nonparametric link function achieves optimal convergence rates, and the structural error variance is obtained. In addition, the results facilitate the construction of confidence regions and hypothesis testing for the unknown parameters. A simulation study is performed and an application to a real dataset is illustrated. The extension to multiple indices is briefly sketched.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/af4c27c4f5bf13fe6e8a52b65fda6eb8037afe3a
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Citations in the corpus
- Estimation for single-index and partially linear single-index integrated models (2016, unknown): “See, for example, Carroll et al. (1997), Xia, Tong and Li (1999), Xia et al. (2002), Yu and Ruppert (2002), Zhu and Xue (2006), Liang et al. (2010), Wang et al. (2010) and Ma and Zhu (2013).”
- GEE analysis of clustered binary data with diverging number of covariates (2011, methodology): “…(1984), this technique was applied to establish the existence and consistency of an M-estimator for i.i.d. k k This approach follows from Theorem 6.3.4 of Ortega and Rheinboldt (1970). data; in a different setting, it was used by Wang et al. (2010) to study a partial linear single-index model.”