# Integrated Variance Reduction Strategies for Simulation

@article{Avramidis1996IntegratedVR, title={Integrated Variance Reduction Strategies for Simulation}, author={Athanassios N. Avramidis and James R. Wilson}, journal={Oper. Res.}, year={1996}, volume={44}, pages={327-346} }

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 expectation, correlation induction including antithetic variates and Latin hypercube sampling, and control variates; all pairings of these techniques are examined. For each integrated strategy, we establish sufficient conditions…

## 83 Citations

### Variance reduction techniques: Experimental comparison and analysis for single systems

- Computer Science
- 2008

It is observed that a variance reduction cannot be guaranteed for every instance a VRT is applied, and that the less-sophisticated techniques often perform better than the relatively more-complex techniques (LHS, PS).

### Combined variance reduction techniques in fully sequential selection procedures

- Mathematics
- 2017

This paper investigates combining various variance reduction techniques into the fully sequential framework, resulting in different R&S procedures with either finite-time or asymptotic statistical validity.

### Correlation-induction techniques for estimating quantiles in simulation experiments

- MathematicsWinter Simulation Conference Proceedings, 1995.
- 1995

A central limit theorem is established for the single-sample estimator based on Latin hypercube sampling, showing that asymptotically this estimator is unbiased and has smaller variance than the comparable direct-simulation estimators based on independent replications.

### Combination of General Antithetic Transformations and Control Variables

- MathematicsMath. Oper. Res.
- 2004

This paper provides conditions under which it can formally prove that the variance is minimized by choosing equal weights and equal control variate coefficients across the different points of evaluation, regardless of the function (integrand) that is evaluated.

### Assessing the Efficiency of Variance Reduction Methods in the Construction Project Network Simulation

- MathematicsIOP Conference Series: Materials Science and Engineering
- 2019

The Monte Carlo simulation has become a standard tool in the practice of planning risk-affected projects. In particular, it is frequently applied to testing the impact of risk on schedule networks…

### A new variance reduction method for option pricing based on sampling the vertices of a simplex

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- 2016

In option pricing based on the Monte Carlo method (MC) (see Boyle (1976)), various techniques for variance reduction are used for computational efficiency. Boyle et al. (1997) summarizes variance…

### Recent Advances in Randomized Quasi-Monte Carlo Methods

- Mathematics, Computer Science
- 2002

The main focus is the applicability of quasi-Monte Carlo (QMC) methods to practical problems that involve the estimation of a high-dimensional integral and how this methodology can be coupled with clever transformations of the integrand in order to reduce the variance further.

### Variance Reduction via Lattice Rules

- Mathematics
- 1999

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…

### Efficiency improvement and variance reduction

- MathematicsProceedings of Winter Simulation Conference
- 1994

Methods such as common random numbers, antithetic variate, control variates, importance sampling, conditional Monte Carlo, stratified sampling, and some others are discussed, as well as the combination of certain of those methods.

### Five-stage procedure for the evaluation of simulation models through statistical techniques

- EconomicsProceedings Winter Simulation Conference
- 1996

This paper recommends the following sequence for the evaluation of simulation models: screening, validation, optimization, uncertainty analysis, and optimization.

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