Smoothed empirical likelihood for quantile regression models with response data missing at random. (English) Zbl 1443.62102
Summary: This paper studies smoothed quantile linear regression models with response data missing at random. Three smoothed quantile empirical likelihood ratios are proposed first and shown to be asymptotically Chi-squared. Then, the confidence intervals for the regression coefficients are constructed without the estimation of the asymptotic covariance. Furthermore, a class of estimators for the regression parameter is presented to derive its asymptotic distribution. Simulation studies are conducted to assess the finite sample performance. Finally, a real-world data set is analyzed to illustrated the effectiveness of the proposed methods.
MSC:
| 62G08 | Nonparametric regression and quantile regression |
| 62G05 | Nonparametric estimation |
| 62G20 | Asymptotic properties of nonparametric inference |
| 62G15 | Nonparametric tolerance and confidence regions |
| 62E20 | Asymptotic distribution theory in statistics |
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