Recently, Sargent and Shanthikumar [5] developed an interesting new variance reduction technique designed to exploit the stochastic structure associated with a cyclic regenerative process. Our purpose here is to study precise conditions under which the confidence intervals proposed in [5] are asymptotically valid. This analysis will provide us with the side benefit of obtaining an optimal way o...

A common class of shielding problems involves the calculation of biological dose rates from large distributed sources at localised positions. A typical example is that of calculating the dose at points around a transport cask. Sophisticated variance reduction methods must be used in Monte Carlo calculations to sample from the important parts of the source and to propagate particles towards the ...

We present results for the strangeness contribution to the nucleon, 〈N|s̄s|N〉 and to the spin of the nucleon, ∆s. By combining several variance reduction techniques for all-to-all propagators we are able to obtain gains in terms of computer time of factors of 25–30 for the disconnected loop that is needed within the calculation of ∆s, relative to the standard approach of just employing time part...

We investigate methods for variance reduction and the elimination of systematic error in a Fourier accelerated Langevin scheme for general spin models. We present results for the SU (3) × SU (3)/SU (3) model in two-dimensions that are consistent with those from multi-grid methods. We argue that the timing for the Langevin method makes it comparable to multi-grid for a given level of error.

In this paper, we propose the calibration procedure for the variance reduction of the stratified Warner’s randomized response estimators, which suggested by Hong et al. (1994) and Kim and Warde (2004), using auxiliary information at the population level. It is shown that the proposed calibration estimators are more efficient than the ordinary Warner’s estimators.

This is a review article on lattice methods for multiple integration over the unit hypercube, with a variance-reduction viewpoint. It also contains some new results and ideas. The aim is to examine the basic principles supporting these methods and how they can be used eeectively for the simulation models that are typically encountered in the area of Management Science. These models can usually ...

Broadie and Glasserman proposed a simulation-based method they named stochastic mesh for pricing highdimensional American options. Based on simulated states of the assets underlying the option at each exercise opportunity, the method produces an estimator of the option value at each sampled state. Under the mild assumption of the finiteness of certain moments, we derive an asymptotic upper boun...

In this contribution we examine certain variance properties of model reduction. The focus is on L2 model reduction, but some general results are also presented. These general results can be used to analyze various other model reduction schemes. The models we study are nite impulse respons (FIR) and output error (OE) models. We compare the variance of two estimated models. The rst one is estimat...