Stochastic Dominance and Transformations of Random Variables
Stochastic Dominance and Transformations of Random Variables
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DOI:
10.1007/978-1-4613-8922-4_3
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发表时间:
1989
期刊:
影响因子:
--
通讯作者:
Jack Meyer
中科院分区:
文献类型:
--
作者:
Jack Meyer
During the past twenty years, the term stochastic dominance (SD) has been used by economists to describe a particular set of rules for ranking random variables. These rules apply to pairs of random variables, and indicate when one is to be ranked higher than the other by specifying a condition which the difference between their cumulative distribution functions (CDF) must satisfy. Various SD ranking procedures have been employed in both empirical and theoretical analysis. First degree stochastic dominance, second degree stochastic dominance, and Rothschild and Stiglitz'definition of increasing risk are prominent examples.A variety of empirical studies have used these SD rules to determine which elements in a choice set are undominated (efficient) in both the pairwise and convex stochastic dominance senses. In addition, theoretical studies involving randomness have often determined the effect of replacing a random parameter by one which dominates it under a particular SD definition. In each of these applications, the SD definitions can be used directly without modification. This is possible since comparing the random variables in a choice set can be carried out by comparing their CDFs; also, replacing one random variable by another in a decision model is accomplished by replacing one CDF with another in the expected utility calculation. Thus, the various SD ranking criteria can be used without modification in these applications.