Distribution approximation by control variable Monte Carlo sampling
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1961
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Abstract
An algorithm is presented which combines the techniques of statistical simulation and numerical integration, thus furnishing improved estimates of cumulative distribution functions. The method uses statistical estimation techniques to form a statistic possessing an approximate normal distribution. A post-stratification sample is used to form a control variable correction for the original stratified numerical estimate, and this combination results in a competitor for stratified Monte Carlo sampling. Examples are presented for known statistical distributions and for an actual electronics system. A branch and bound algorithm, which can reduce the amount of computations necessary for both control variable stratified Monte Carlo sampling, is developed for monotone functions.