Computational Methods for Measuring the Difference of Empirical Distributions

This paper presents a simple computational method for measuring the difference of independent empirical distributions estimated by bootstrapping or other resampling approaches. Using data from a field test of external scope in contingent valuation, this complete combinatorial method is compared with other methods (empirical convolutions, repeated sampling, normality, nonoverlapping confidence intervals) that have been suggested in the literature. Tradeoffs between methods are discussed in terms of programming complexity, time and computer resources required, bias, and the precision of the estimate. Copyright 2005, Oxford University Press.

Inference on the ratio of two coefficients of variation of two lognormal distributions

The coefficient of variation (CV) can be used as an index of reliability of measurement. The lognormal distribution has been applied to fit data in many fields. We developed approximate interval estimation of the ratio of two coefficients of variation (CsV) for lognormal distributions by using the Wald-type, Fieller-type, log methods, and method of variance estimates recovery (MOVER). The simulation studies show that empirical coverage rates of the methods are satisfactorily close to a nominal coverage rate for medium sample sizes

Inferences for the ratio: Fieller’s interval, log ratio, and large sample based confidence intervals

Fieller’s interval, Ratio estimation, Variance estimation, Sample surveys, Small sample inference,