Organ transplantation policy evaluation
WSC '95 Proceedings of the 27th conference on Winter simulation
Estimating and simulating Poisson processes with trends or asymmetric cyclic effects
Proceedings of the 29th conference on Winter simulation
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To automate the multiresolution procedure of Kuhl and Wilson for modeling and simulating arrival processes that exhibit long-term trends and nested periodic effects (such as daily, weekly, and monthly cycles), we present a statistical-estimation method that involves the following steps at each resolution level corresponding to a basic cycle: (a) transforming the cumulative relative frequency of arrivals within the cycle (for example, the percentage of all arrivals as a function of the day of the week within the weekly cycle) to obtain a statistical model with normal, constant-variance responses; (b) fitting a specially formulated polynomial to the transformed responses; (c) performing a likelihood ratio test to determine the degree of the fitted polynomial; and (d) fitting a polynomial of the degree determined in (c) to the original (untransformed) responses. An example demonstrates web-based software that implements this flexible approach to handling complex arrival processes.