Transformed-linear prediction for extremes

Jeongjin Lee, Daniel Cooley

    Research output: Working paperPreprint

    13 Downloads (Pure)


    We consider the problem of performing prediction when observed values are at their highest levels. We construct an inner product space of nonnegative random variables from transformed-linear combinations of independent regularly varying random variables. The matrix of inner products corresponds to the tail pairwise dependence matrix, which summarizes tail dependence. The projection theorem yields the optimal transformed-linear predictor, which has the same form as the best linear unbiased predictor in non-extreme prediction. We also construct prediction intervals based on the geometry of regular variation. We show that these intervals have good coverage in a simulation study as well as in two applications; prediction of high pollution levels, and prediction of large financial losses.
    Original languageEnglish
    Publication statusPublished - 5 Nov 2021


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