NDLI: Simultaneous confidence interval for quantile regression

Content Provider	Springer Nature Link
Author	Lim, Yaeji Oh, Hee Seok
Copyright Year	2014
Abstract	This paper considers a problem of constructing simultaneous confidence intervals for quantile regression. Recently, Krivobokova et al. (J Am Stat Assoc 105:852–863, 2010) provided simultaneous confidence intervals for penalized spline estimator. However, it is well known that the conventional mean-based penalized spline and its confidence intervals collapse when data are not normally distributed such as skewed or heavy-tailed, and hence, the resultant confidence intervals further provide low coverage probability. To overcome this problem, this paper proposes a new approach that constructs simultaneous confidence intervals for penalized quantile spline estimator, which yields a desired coverage probability. The results obtained from numerical experiments and real data validate the effectiveness of the proposed method.
Starting Page	345
Ending Page	358
Page Count	14
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	30
Issue Number	2
e-ISSN	16139658
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-10-18
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Penalized spline Pseudo data Quantile loss Simultaneous confidence interval Statistics Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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