Back to Search
Start Over
Multiplier bootstrap for quantile regression: non-asymptotic theory under random design
- Source :
- Information and Inference: A Journal of the IMA, vol 10, iss 3, Information and Inference A Journal of the IMA, vol 10, iss 3
- Publication Year :
- 2021
- Publisher :
- eScholarship, University of California, 2021.
-
Abstract
- This paper establishes non-asymptotic concentration bound and Bahadur representation for the quantile regression estimator and its multiplier bootstrap counterpart in the random design setting. The non-asymptotic analysis keeps track of the impact of the parameter dimension $d$ and sample size $n$ in the rate of convergence, as well as in normal and bootstrap approximation errors. These results represent a useful complement to the asymptotic results under fixed design and provide theoretical guarantees for the validity of Rademacher multiplier bootstrap in the problems of confidence construction and goodness-of-fit testing. Numerical studies lend strong support to our theory and highlight the effectiveness of Rademacher bootstrap in terms of accuracy, reliability and computational efficiency.
- Subjects :
- Statistics and Probability
Numerical Analysis
Statistics::Theory
quantile regression
Applied Mathematics
concentration inequality
Estimator
010103 numerical & computational mathematics
robustness
01 natural sciences
Quantile regression
010104 statistics & probability
multiplier bootstrap
Computational Theory and Mathematics
Rate of convergence
confidence interval
Sample size determination
goodness-of-fit test
Applied mathematics
Statistics::Methodology
Multiplier (economics)
Bahadur representation
0101 mathematics
Analysis
Mathematics
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Information and Inference: A Journal of the IMA, vol 10, iss 3, Information and Inference A Journal of the IMA, vol 10, iss 3
- Accession number :
- edsair.doi.dedup.....dc48ca7b423623287609c3988f0bfe56