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Empirical likelihood in single-index quantile regression with high dimensional and missing observations.
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- Author(s): Wang, Bao-Hua1 (AUTHOR); Liang, Han-Ying1 (AUTHOR)
- Source:
Journal of Statistical Planning & Inference. Sep2023, Vol. 226, p1-19. 19p.- Subject Terms:
- Source:
- Additional Information
- Abstract: 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]
- Abstract: Copyright of Journal of Statistical Planning & Inference is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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