Kyriazopoulou-Panagiotopoulou, S and Kontoyiannis, I and Meyn, SP (2008) *Control variates as screening functions.* In: UNSPECIFIED.

## Abstract

Suppose that the mean μ = E[F(X)] of a given function F : &Rdbl; → &Rdbl; is to be estimated by the empirical average Ŝ n := 1/n∑n i=1 F(Xi) of the values F(Xi), where X1,X2, . . . , Xn are independent samples distributed like X. In cases when the mean ν= E[U(X)] of a different function U : &Rdbl → &Rdbl is known, we introduce a sampling rule, called the "screened estimator," which states that we should only consider estimates that correspond to times n when the empirical average of the {U(Xi)} is sufficiently close to its known mean. Under the assumption that U dominates F in an appropriate sense, it is shown that the screened estimates admit exponential error bounds, even when F(X) is heavy-tailed. A geometric interpretation, in the spirit of Sanov's theorem, is given for this fact, and nonasymptotic, explicit exponential bounds for the screened estimates are derived. The mathematical tools used in the analysis consist, primarily, of large deviations techniques. A detailed Markov Chain Monte Carlo (MCMC) simulation example illustrates that, in certain MCMC scenarios, screening can be very effective in terms of variance reduction, even in cases where the standard technique of control variates fails. © 2008 ICST ISBN.

Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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Subjects: | UNSPECIFIED |

Divisions: | Div F > Signal Processing and Communications |

Depositing User: | Cron Job |

Date Deposited: | 08 Jan 2018 20:12 |

Last Modified: | 18 Aug 2020 12:42 |

DOI: | 10.4108/ICST.VALUETOOLS2008.4242 |