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Empirical likelihood in single-index quantile regression with high dimensional and missing observations.
- Source :
-
Journal of Statistical Planning & Inference . Sep2023, Vol. 226, p1-19. 19p. - Publication Year :
- 2023
-
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]
Details
- Language :
- English
- ISSN :
- 03783758
- Volume :
- 226
- Database :
- Academic Search Index
- Journal :
- Journal of Statistical Planning & Inference
- Publication Type :
- Academic Journal
- Accession number :
- 162440267
- Full Text :
- https://doi.org/10.1016/j.jspi.2023.01.005