Variance Reduction

COMMON RANDOM NUMBERS (CRN)

The common random numbers variance reduction technique is a popular and useful variance reduction technique which applies when we are comparing two or more alternative configurations (of a system) instead of investigating a single configuration. CRN has also been called ''Correlated sampling'', ''Matched streams'' or ''Matched pairs''.

CRN requires synchronization of the random number streams, which ensures that in addition to using the same random numbers to simulate all configurations, a specific random number used for a specific purpose in one configuration is used for exactly the same purpose in all other configurations. For example, in queueing theory, if we are comparing two different configurations of tellers in a bank, we would want the (random) time of arrival of the ''N''th customer to be the same for both configurations.

UNDERLYING PRINCIPLE OF THE CRN TECHNIQUE

Suppose

X_{1j}

and

X_{2j}

are the observations from the first and second configurations on the ''j''th independent replication.

We want to estimate
:

\xi= E(X_{1j})-E(X_{2j})=\mu_1-\mu_2. \,

If we perform ''n'' replications of each configuration and let
:

Z_j=X_{1j}-X_{2j} \quad\mbox{for } j=1,2,\ldots, n,

then

E(Z_j)=\xi

and ''Z''(''n'') = Σ ''Z''_''j'' / ''n'' is an unbiased estimator of

\xi

.

And since the

Z_j

's are independent identically distributed random variables,
:

\operatorname{Var}

{Link without Title} = rac{\operatorname{Var}(Z_j)}{n}.

In case of independent sampling, i.e., no common random numbers used then Cov(''X''_1''j'', ''X''_2''j'') = 0. But if we succeed to induce an element of positive correlation between ''X''₁ and ''X''₂ such that Cov(''X''_1''j'', ''X''_2''j'') > 0, it can be seen from the equation above that the variance is reduced.

It can also be observed that if the CRN induces a negative correlation, i.e., Cov(''X''_1''j'', ''X''_2''j'') < 0, this technique can actually backfire, where the variance is increased and not decreased (as intended).

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	CATEGORIES ABOUT VARIANCE REDUCTION

	monte carlo methods

Information About

Variance Reduction