New power limits for extremes

For order statistics there is a deceptively simple link between affine and power norming, using exponential transforms. This link does not tell the whole story about limit distributions. The exponential transforms W=eV and W=−e−V yield limit variables which are either positive or negative. Under power norming there exist discrete limit distributions for maxima. The corresponding limit variables assume two values, one of which is zero. All variables with two values, one positive, one zero, are power limits for maxima. They are of different power type if they give different weight to zero, but they all have the same domain, the set of dfs with finite positive upper endpoint and an upper tail which varies slowly. So we see that convergence of types does not hold for power norming. This paper gives a classification of the power limits and their domains for maxima, variables conditioned to be large, and POTs (where power limits may assume three values). Convergence of sample clouds under power norming is studied, and of intermediate upper order statistics.

The new power limits do not affect applications. Power norming is a viable alternative to classic extreme value theory. The extra norming constant in the exponent automatically improves the rate of convergence. Hill plots are a good instrument to determine this norming constant. It will be shown how to eliminate the bias of Hill plots and estimate high upper quantiles when the tail does not vary regularly or when convergence is slow.