VARIANCE REDUCTION ANALYSIS

Variance Reduction via Lattice

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 ...

Variance Reduction Techniques in . . .

L2 Model reduction and variance reduction

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...

Weighted Maximum Variance Dimensionality Reduction

Dimensionality reduction procedures such as principal component analysis and the maximum margin criterion discriminant are special cases of a weighted maximum variance (WMV) approach. We present a simple two parameter version of WMV that we call 2P-WMV. We study the classification error given by the 1-nearest neighbor algorithm on features extracted by our and other dimensionality reduction met...

Introduction to variance reduction methods

All the results of the preceding lecture show that the ratio σ/ √ N governs the accuracy of a Monte-Carlo method with N simulations. An obvious consequence of this fact is that one always has interest to rewrite the quantity to compute as the expectation of a random variable which has a smaller variance : this is the basic idea of variance reduction techniques. For complements, we refer the rea...