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Simultaneous confidence interval for quantile regression

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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.

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Acknowledgments

This work was supported by the National Research Foundation (NRF) grant funded by the Korea government (MSIP) (Nos. 2012002717 and 2011-0030811).

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Authors and Affiliations

  1. Department of Statistics, Seoul National University, Seoul, 151-747, Korea

    Yaeji Lim & Hee-Seok Oh

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  1. Yaeji Lim
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  2. Hee-Seok Oh
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Correspondence to Hee-Seok Oh.

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Lim, Y., Oh, HS. Simultaneous confidence interval for quantile regression. Comput Stat 30, 345–358 (2015). https://doi.org/10.1007/s00180-014-0537-7

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  • DOI: https://doi.org/10.1007/s00180-014-0537-7

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