One variable is said to ``stochastically dominate'' another if the probability of observations smaller than x is greater for one variable than the other, for all x. Inferring stochastic dominance from data samples is important for many applications of econometrics and experimental psychology, but little is known about the performance of existing inferential methods. Through simulation, we show that three of the most widely used inferential methods are inadequate for use in small samples of the size commonly encountered in many applications (up to 400 observations from each distribution). We develop two new inferential methods that perform very well in a limited, but practically important, case where the two variables are guaranteed not to be equal in distribution. We also show that extensions of these new methods, and an improved version of an existing method, perform quite well in the original, unlimited case.

Item Type:	Refereed Article
Keywords:	Stochastic dominance, Semiparametric and non-parametric methods, Bootstrap methods, Bayesian analysis
Research Division:	Psychology and Cognitive Sciences
Research Group:	Cognitive Sciences
Research Field:	Decision Making
Objective Division:	Expanding Knowledge
Objective Group:	Expanding Knowledge
Objective Field:	Expanding Knowledge in Psychology and Cognitive Sciences
Author:	Heathcote, A (Professor Andrew Heathcote)
ID Code:	98894
Year Published:	2010
Deposited By:	Medicine (Discipline)
Deposited On:	2015-03-06
Last Modified:	2015-05-11
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