Fundamentals of statistical exponential families: with applications in statistical decision theory
Fundamentals of statistical exponential families: with applications in statistical decision theory
Large deviations theory techniques in Monte Carlo simulation
WSC '89 Proceedings of the 21st conference on Winter simulation
Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension
Journal of Combinatorial Theory Series A
Small Variance Estimators for Rare Event Probabilities
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
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An acceptance-rejection algorithm for the simulation of random variables in statistical exponential families is described. This algorithm does not require any prior knowledge of the family, except sufficient stati stics and the value of the parameter. It allows simulation from many members of the exponential family. We present some bounds on computing time, as well as the main properties of the empirical measures of samples simulated by our methods (functional Glivenko-Cantelli and central limit theorems). This algorithm is applied in order to evaluate the distribution of M-estimators under composite alternatives; we also propose its use in Bayesian statistics in order to simulate from posterior distributions.