An Efficient Method for Generating Discrete Random Variables with General Distributions
ACM Transactions on Mathematical Software (TOMS)
A comparison of three methods of modeling input distributions
WSC '81 Proceedings of the 13th conference on Winter simulation - Volume 1
Synthetic Traces for Trace-Driven Simulation of Cache Memories
IEEE Transactions on Computers
WSC '94 Proceedings of the 26th conference on Winter simulation
Input modeling when simple models fail
WSC '95 Proceedings of the 27th conference on Winter simulation
Using univariate Be´zier distributions to model simulation input processes
WSC '93 Proceedings of the 25th conference on Winter simulation
Input modeling tools for complex problems
Proceedings of the 30th conference on Winter simulation
Alternative approaches for specifying input distributions and processes (panel session)
WSC' 90 Proceedings of the 22nd conference on Winter simulation
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A regression method for estimating the inverse of a continuous cumulative probability function F(x) is presented. It is assumed that an ordered sample, X1, …, Xn, of identically and independently distributed random variables is available. A reference distribution F0(x) with known inverse F0-1(p) is used to calculate the quantities Wi = i ln[F0(Xi)/F0(Xi+1)]. These quantities are used to estimate the function &ggr;(p) = pd ln≥F0[F-1(p)]⋦/dp from which an estimate of F-1(p) is derived. The method produces an estimate in a form that is convenient for random variate generation. The procedure is illustrated using data from a study of oil and gas lease bidding.