Improving point and interval estimators of monotone functions by rearrangement

V. Chernozhukov, I. Fernández-Val, A. Galichon

    Research output: Contribution to journalArticle


    Suppose that a target function is monotonic and an available original estimate of this target function is not monotonic. Rearrangements, univariate and multivariate, transform the original estimate to a monotonic estimate that always lies closer in common metrics to the target function. Furthermore, suppose an original confidence interval, which covers the target function with probability at least 1-α, is defined by an upper and lower endpoint functions that are not monotonic. Then the rearranged confidence interval, defined by the rearranged upper and lower endpoint functions, is monotonic, shorter in length in common norms than the original interval, and covers the target function with probability at least 1-α. We illustrate the results with a growth chart example.

    Original languageEnglish (US)
    Pages (from-to)559-575
    Number of pages17
    Issue number3
    StatePublished - Sep 1 2009



    • Growth chart
    • Improved estimation
    • Improved inference
    • Isotonization
    • Lorentz inequality
    • Monotone function
    • Multivariate
    • Quantile regression
    • Rearrangement

    ASJC Scopus subject areas

    • Statistics and Probability
    • Mathematics(all)
    • Agricultural and Biological Sciences (miscellaneous)
    • Agricultural and Biological Sciences(all)
    • Statistics, Probability and Uncertainty
    • Applied Mathematics

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