Simultaneous confidence interval for quantile regression

Yaeji Lim and Hee-Seok Oh ()

Computational Statistics, 2015, vol. 30, issue 2, 345-358

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. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Penalized spline; Pseudo data; Quantile loss; Simultaneous confidence interval (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:30:y:2015:i:2:p:345-358

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DOI: 10.1007/s00180-014-0537-7

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