Premium
A STOCHASTIC DOMINANCE ALGORITHM USING PIECEWISE LINEAR APPROXIMATIONS
Author(s) -
Ritchken Peter H.,
Agarwal Yogesh,
Gupta Alok
Publication year - 1985
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1985.tb01489.x
Subject(s) - stochastic dominance , cumulative distribution function , mathematics , piecewise , dominance (genetics) , piecewise linear function , mathematical optimization , random variable , algorithm , computer science , statistics , probability density function , mathematical analysis , biochemistry , chemistry , gene
Current stochastic dominance algorithms use step functions to approximate the cumulative distributions of the alternatives, even when the underlying random variables are known to be continuous. Since stochastic dominance tests require repeated integration of the cumulative distribution functions, a compounding of errors may result from this type of approximation. This article introduces a new stochastic dominance algorithm that approximates the cumulative distribution function by piecewise linear approximations. Comparisons between the new and old algorithms are performed for normally distributed alternatives. In about 95 percent of all cases, the two algorithms produce the same result.