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Computational Methods for Measuring the Difference of Empirical Distributions
Author(s) -
Poe Gregory L.,
Giraud Kelly L.,
Loomis John B.
Publication year - 2005
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.1111/j.1467-8276.2005.00727.x
Subject(s) - bootstrapping (finance) , resampling , normality , computer science , confidence interval , sampling (signal processing) , field (mathematics) , statistics , mathematics , econometrics , algorithm , filter (signal processing) , computer vision , pure mathematics
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.