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Variance Reduction for Bernouilli Response Variables

Esteban Vegas, Jordi Ocaña

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Abstract

A method to reduce the sampling errors in simulations performed to estimate the expectation of a dichotomous variable is suggested. It is equivalent to the variance reduction technique known as Control Variates. The new estimator is unbiased. Some ways to estimate its variance (and to estimate the true amount of variance reduction) are suggested. A simulation study (a sort of simulation into a simulation, requiring the use of supercomputing techniques) is presented in order to show the validity of this approach in the determination of the power of a test.

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What this paper is about

A method to reduce the sampling errors in simulations performed to estimate the expectation of a dichotomous variable is suggested. It is equivalent to the variance reduction technique known as Control Variates. The new estimator is unbiased. Some ways to estimate its variance (and to estimate the true amount of variance reduction) are suggested. A simulation study (a sort of simulation into a simulation, requiring the use of supercomputing techniques) is presented in order to show the validity of this approach in the determination of the power of a test.

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Available abstract

A method to reduce the sampling errors in simulations performed to estimate the expectation of a dichotomous variable is suggested. It is equivalent to the variance reduction technique known as Control Variates. The new estimator is unbiased. Some ways to estimate its variance (and to estimate the true amount of variance reduction) are suggested. A simulation study (a sort of simulation into a simulation, requiring the use of supercomputing techniques) is presented in order to show the validity of this approach in the determination of the power of a test.

Key concepts: Control variates, Variance reduction, Variance (accounting), Statistics, Estimator, sort, Reduction (mathematics), Bias of an estimator

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