缺失数据下基于经验似然的加权复合分位数回归推断

Abstract

Abstract:

We proposed a weighted composite quantile regression method based on empirical likelihood in linear model with some covariates missing at random, and proved the large sample properties of the proposed method under the missing at random mechanism. The results show that the proposed method is computationally simple and the estimation efficiency of the regression parameters is higher than that of the inverse probability weighted method.

Key words: linear model, missing at random, empirical likelihood, composite quantile regression, inverse probability weighting

CLC Number:

O212.4

YUAN Xiaohui, ZHAO Xuedong. Estimation of Weighted Composite Quantile Regression withMissing Covariates Based on Empirical Likelihood[J].Journal of Jilin University Science Edition, 2016, 54(05): 1008-1016.

[1]	Wang Haobai, Cheng Jianhua. Statistical Properties of Almost Unbiased Liu Estimator under Balanced Loss Function [J]. Journal of Jilin University Science Edition, 2026, 64(4): 780-0788.
[2]	WANG Wenshan, ZHAO Shishun. Statistical Properties of Almost Unbiased Estimateunder Balance Loss Function [J]. Journal of Jilin University Science Edition, 2018, 56(4): 871-876.
[3]	YU Zhuoxi, LI Mengli. Variable Selection Based on Empirical Likelihood forPartially Linear Models with Longitudinal Data [J]. Journal of Jilin University Science Edition, 2018, 56(4): 877-882.
[4]	YUAN Xiaohui, JU Tingting. Weighted Quantile Regression for VaryingCoefficient Modelswith Missing Covariates Based on Empirical Likelihood [J]. Journal of Jilin University Science Edition, 2017, 55(02): 281-288.
[5]	YUAN Xiaohui, LIU Tianqing. Weighted Quantile Regression Based on InducedSmoothing Method with Missing Covariates [J]. Journal of Jilin University Science Edition, 2016, 54(06): 1314-1322.
[6]	YUAN Xiaohui, LIU Tianqing. Empirical Likelihood Inference for MeasurementError Model with Multiple Populations [J]. Journal of Jilin University Science Edition, 2016, 54(02): 266-272.
[7]	ZHAO Zhiwen, BI Li, ZHANG Meili. Stationarity Test for Poisson Autoregressive Model [J]. Journal of Jilin University Science Edition, 2015, 53(02): 235-240.
[8]	PENG Cuixin, DU Bochuan. Test for Linear Regression Model Parameter andRegression Analysis for the Polyclonal Antibody Titer [J]. Journal of Jilin University Science Edition, 2013, 51(05): 845-848.
[9]	HAN Yu, JIN Ying-Hua, TUN Wu-Qing. Statistical Inference for Autoregressive ConditionalDuration Models Based on Empirical Likelihood [J]. J4, 2013, 51(02): 219-225.
[10]	XU Zhuo-Xi, WANG De-Hui. Empirical Likelihood in Partial Linear Modelswith MA(∞) Error Process [J]. J4, 2011, 49(04): 615-624.
[11]	ZHANG Hai-Xiang, WANG De-Hui. Empirical Likelihood Inference for the StationaryARIMA(p,d,q) Model [J]. J4, 2009, 47(05): 871-876.
[12]	QIU Hongbing, LUO Ji. On Robustness of GLSE in Terms of Error Distributionsin Linear Model [J]. J4, 2009, 47(01): 13-16.
[13]	ZHU Fukang, WANG Dehui, LI Fengxiang, LI Han. Empirical Likelihood Inference foran Integervalued ARCH(p) Model [J]. J4, 2008, 46(06): 1042-1048.
[14]	HE Daojiang, GUO Dawei. Admissibility for Linear Estimators in a Multivariate Linear Model with Respect to an Inequality Restriction [J]. J4, 2007, 45(05): 738-742.
[15]	SHI Sanzhi,, SONG Lixin. Generalized Likelihood Ratio Test in Partially Linear Models [J]. J4, 2007, 45(01): 56-62.

Estimation of Weighted Composite Quantile Regression withMissing Covariates Based on Empirical Likelihood

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 10