A unified view of the IPA, SF, and LR gradient estimation techniques
Management Science
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Van der Corput sequences, Kakutani transforms and one-dimensional numerical integration
Journal of Computational and Applied Mathematics
Efficiency improvement and variance reduction
WSC '94 Proceedings of the 26th conference on Winter simulation
Two approaches for estimating the gradient in functional form
WSC '93 Proceedings of the 25th conference on Winter simulation
Monto Carlo extension of quasi-Monte Carlo
Proceedings of the 30th conference on Winter simulation
Efficiency improvement by lattice rules for pricing Asian options
Proceedings of the 30th conference on Winter simulation
The effective dimension and quasi-Monte Carlo integration
Journal of Complexity
Encyclopedia of Optimization
Mathematical and Computer Modelling: An International Journal
Efficient Generation of Parallel Quasirandom Faure Sequences Via Scrambling
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Restricted likelihood inference for generalized linear mixed models
Statistics and Computing
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Simulated maximum likelihood estimates an analytically intractable likelihood function with an empirical average based on data simulated from a suitable importance sampling distribution. In order to use simulated maximum likelihood in an efficient way, the choice of the importance sampling distribution as well as the mechanism to generate the simulated data are crucial. In this paper we develop a new heuristic for an automated, multistage implementation of simulated maximum likelihood which, by adaptively updating the importance sampler, approximates the (locally) optimal importance sampling distribution. The proposed approach also allows for a convenient incorporation of quasi-Monte Carlo methods. Quasi-Monte Carlo methods produce simulated data which can significantly increase the accuracy of the likelihood-estimate over regular Monte Carlo methods. Several examples provide evidence for the potential efficiency gain of this new method. We apply the method to a computationally challenging geostatistical model of online retailing.