|M.Sc Student||Man Rostislav|
|Subject||Rare Event Simulation for Heavy-Tailed Distributions Using|
|Department||Department of Industrial Engineering and Management||Supervisors||Professor Emeritus Reuven-Yacov Rubinstein (Deceased)|
|Professor Arkadi Nemirovski|
We present a novel method, called the transform likelihood ratio (TLR) method, for estimation of rare event probabilities with heavy-tailed distributions. Via a simple transformation (change of variables) technique the TLR method reduces the original rare event probability estimation with heavy tail distributions to an equivalent one with light tail distribution, such as the uniform or exponential distribution. Once this transformation has been established we estimate the rare event probability via importance sampling, using the classical exponential change of measure or the standard likelihood ratio change of measure. In the latter case the importance sampling distribution is chosen from the same parametric family as the transformed distribution. We estimate the optimal parameter vector of the importance sampling distribution using the cross-entropy method. We prove the polynomial complexity of the TLR method for certain heavy-tailed models and demonstrate numerically its high efficiency for various heavy-tailed models previously thought to be intractable. We also show that the TLR method can be viewed as a universal tool in the sense that not only it provides a unified view for heavy-tailed simulation but also can be efficiently used in simulation with light-tailed distributions. We present the Big-Step CE Method that is aimed at speeding up the algorithms based on the CE Method. We also present extensive simulation results which support the efficiency of the TLR method.