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Using linear interpolation to reduce the order of the coverage error of nonparametric prediction intervals based on right-censored data

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  • Beutner, E.
  • Cramer, E.

Abstract

We prove a general result showing that a simple linear interpolation between adjacent random variables reduces the coverage error of nonparametric prediction intervals for a future observation from the same underlying distribution function from O(n−1) to O(n−2). To illustrate the result we show that it can be applied to various scenarios of right censored data including Type-II censored samples, pooled Type-II censored data, and progressively Type-II censored order statistics. We further illustrate the result by simulations indicating that the desired level of significance is almost attained for moderate sample sizes.

Suggested Citation

  • Beutner, E. & Cramer, E., 2014. "Using linear interpolation to reduce the order of the coverage error of nonparametric prediction intervals based on right-censored data," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 95-109.
    • Handle: RePEc:eee:jmvana:v:129:y:2014:i:c:p:95-109
    DOI: 10.1016/j.jmva.2014.04.007
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    References listed on IDEAS

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    1. N. Balakrishnan & E. Beutner & E. Cramer, 2013. "Computational aspects of statistical intervals based on two Type-II censored samples," Computational Statistics, Springer, vol. 28(3), pages 893-917, June.
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