Simulating heavy tailed processes using delayed hazard rate twisting
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation of right and left truncated gamma distributions by mixtures
Statistics and Computing
The Transform Likelihood Ratio Method for Rare Event Simulation with Heavy Tails
Queueing Systems: Theory and Applications
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Adaptive methods for sequential importance sampling with application to state space models
Statistics and Computing
Adaptive independence samplers
Statistics and Computing
Quick simulation: a review of importance sampling techniques in communications systems
IEEE Journal on Selected Areas in Communications
Markov chain importance sampling with applications to rare event probability estimation
Statistics and Computing
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The cross-entropy (CE) method is an adaptive importance sampling procedure that has been successfully applied to a diverse range of complicated simulation problems. However, recent research has shown that in some high-dimensional settings, the likelihood ratio degeneracy problem becomes severe and the importance sampling estimator obtained from the CE algorithm becomes unreliable. We consider a variation of the CE method whose performance does not deteriorate as the dimension of the problem increases. We then illustrate the algorithm via a high-dimensional estimation problem in risk management.