Semiparametric efficient estimation for partially linear single-index models with responses missing at random
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DOI: 10.1016/j.jmva.2014.03.001
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Cited by:
- Jianglin Fang & Wanrong Liu & Xuewen Lu, 2018. "Empirical likelihood for heteroscedastic partially linear single-index models with growing dimensional data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(3), pages 255-281, April.
- Fangfang Li & Hui Sun & Yu Gu & Ge Yu, 2022. "A Noise-Aware Multiple Imputation Algorithm for Missing Data," Mathematics, MDPI, vol. 11(1), pages 1-16, December.
- Luo, Wei & Cai, Xizhen, 2016. "A new estimator for efficient dimension reduction in regression," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 236-249.
- Lai, Peng & Zhang, Qingzhao & Lian, Heng & Wang, Qihua, 2016. "Efficient estimation for the heteroscedastic single-index varying coefficient models," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 84-93.
- Wei, Yuting & Wang, Qihua, 2021. "Cross-validation-based model averaging in linear models with response missing at random," Statistics & Probability Letters, Elsevier, vol. 171(C).
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Keywords
Efficient score function; Estimating equations; Heteroscedasticity; Missing at random; Partially linear single-index model;All these keywords.
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