Simulating medical decision trees with random variable parameters
WSC '92 Proceedings of the 24th conference on Winter simulation
WSC '94 Proceedings of the 26th conference on Winter simulation
Using univariate Be´zier distributions to model simulation input processes
WSC '93 Proceedings of the 25th 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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To generate random variates from an unknown continuous distribution via the inverse transform method, we present a flexible, computationally tractable procedure for estimating the associated inverse distribution function based on sample data. Previously proposed methods for estimating inverse distribution functions can fail in either the distribution-fitting or variate-generation stages of application. To avoid these difficulties, we have developed the procedure IDPF for estimating an Inverse Distribution with a Polynomial Filter. After a first-cut or reference distribution has been obtained by some standard technique, a front-end polynomial filter for the inverse of the reference distribution is estimated by constrained nonlinear regression so that the resulting inverse distribution has minimum "distance" from the empirical inverse distribution. The constraints on the regression ensure that the fitted inverse distribution function is nondefective and monotonically nondecreasing. A specific implementation of this procedure is based on well-known techniques for obtaining a reference fit from the Johnson translation system of distributions. We present the results of a Monte Carlo study to demonstrate the effectiveness of the method. Compared to the reference fit, procedure IDPF yields significantly better approximations not only to the empirical inverse distribution function but also to the underlying theoretical inverse distribution function.