approximation of I than does I N . There are numerous variants on this technique. This section takes up four that account for most use of the method: antithetic variables, systematic sampling, conditional expectations, and control variables. The scope for combining these variance reduction techniques

than does I N . There are numerous variants on this technique. This section takes up four that account for most use of the method: antithetic variables, systematic sampling, conditional expectations, and control variables. The scope for combining these variance reduction techniques with the methods

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We give an overview of the main techniques for improving the statistical efficiency of simulation estimators. Efficiency improvement is typically (but not always) achieved through variance reduction. We discuss methods such as common random numbers, antithetic variates, control variates, importance

We develop a variance reduction method for smoothing splines. We do this by showing that the quadratic interpolation method introduced in Cheng et al. (2006), for local linear estimators, also works for smoothing splines. For a given point of estimation, Cheng et al. (2006) define a variance

Citation for published version (APA): Kleijnen, J. P. C., & Rubinstein, R. Y. (1994). Monte Carlo sampling and variance reduction techniques. (CentER Discussion Paper; Vol. 1994-1). Unknown Publisher. General rights Copyright and moral rights for the publications made accessible in the public

the advantage of reducing the overall variance of the system. Instead of using multiple classi ers, we propose here to examine other possible means of variance reduction (VR), namely through the use of multiple real samples, synthetic samples, dierent extractors (features) and biometric modalities. It is found

"Knowledge of either analytical or numerical approximations should enable more efficient simulation estimators to be constructed." This principle seems intuitively plausible and certainly attractive, yet no completely satisfactory general methodology has been developed to exploit it. We present a new approach for Markov processes that relies on the construction of a martingale that is strongly correlated with the performance measure of interest. As such, the method may be viewed as a special case of the internal control variates efficiency improvement technique. Introduction "Knowledge of either analytical or numerical approximations should enable more efficient simulation estimators to be constructed." This principle seems intuitively plausible and certainly attractive. Nevertheless, no completely satisfactory general methodology has been developed to exploit it. In this paper, we present a new approach. Our methodology applies to Markov processes in discrete or continuous time, and ...

We develop strategies for integrated use of certain well-known variance reduction techniques to estimate a mean response in a finite-horizon simulation experiment. The building blocks for these integrated variance reduction strategies are the techniques of conditional expec-tation, correlation