Empirical likelihood in single-index quantile regression with high dimensional and missing observations.

Based on empirical likelihood method, we investigate statistical inference in partially linear single-index quantile regression with high dimensional linear and single-index parameters when the observations are missing at random, which allows the response or covariates or response and covariates simultaneously missing. In particular, applying B-spline approximation to the unknown link function, we establish asymptotic normality of bias-corrected empirical likelihood ratio function and maximum empirical likelihood estimators of the parameters. Variable selection is considered by using the SCAD penalty. Meanwhile, we propose a penalized empirical likelihood ratio statistic to test hypothesis, and prove its asymptotically chi-square distribution under the null hypothesis. Also, simulation study and a real data analysis are conducted to evaluate the performance of the proposed methods. • Empirical likelihood for partially linear single-index quantile regression model. • High dimensional statistical inference with observations missing at random. • Hypothesis testing based on penalized empirical likelihood ratio statistic. [ABSTRACT FROM AUTHOR]

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