Expected spacing

Abstract

The expected spacing, or average difference between consecutive order statistics, is known for uniform and exponential random variates. For other distributions, we can estimate it using the derivative of the inverse cumulative density (quantile) function, since passing a uniformly drawn value, whose spacing we know, through this function generates a random value from the distribution, and the difference between two such uniform values approximates the derivative. We calculate the spacing for two new distributions, the logistic and Gumbel, and show the estimator is exact for the first and approximate for the second. Comparing the estimators for six other distributions to numeric simulations shows they are also approximations, best in the middle of the order statistics with an error that goes inversely with the square of the sample size, but degrading in the tails.