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

E. Beutner and E. Cramer

Journal of Multivariate Analysis, 2014, vol. 129, issue C, 95-109

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.

Keywords: Nonparametric prediction intervals; Right-censored data; Asymptotic refinements (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1016/j.jmva.2014.04.007

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