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Variable selection via composite quantile regression with dependent errors.
- Source :
-
Statistica Neerlandica . Feb2015, Vol. 69 Issue 1, p1-20. 20p. - Publication Year :
- 2015
-
Abstract
- We propose composite quantile regression for dependent data, in which the errors are from short-range dependent and strictly stationary linear processes. Under some regularity conditions, we show that composite quantile estimator enjoys root- n consistency and asymptotic normality. We investigate the asymptotic relative efficiency of composite quantile estimator to both single-level quantile regression and least-squares regression. When the errors have finite variance, the relative efficiency of composite quantile estimator with respect to the least-squares estimator has a universal lower bound. Under some regularity conditions, the adaptive least absolute shrinkage and selection operator penalty leads to consistent variable selection, and the asymptotic distribution of the non-zero coefficient is the same as that of the counterparts obtained when the true model is known. We conduct a simulation study and a real data analysis to evaluate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00390402
- Volume :
- 69
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Statistica Neerlandica
- Publication Type :
- Academic Journal
- Accession number :
- 100420584
- Full Text :
- https://doi.org/10.1111/stan.12035