In statistics, the antithetic variates method is a variance reduction technique used in Monte Carlo methods. Considering that the error in the simulated signal (using Monte Carlo methods) has a one-over square root convergence, a very large number of sample paths is required to obtain an accurate result. The antithetic variates method reduces the variance of the simulation results.^[1][2]

Underlying principle

The antithetic variates technique consists, for every sample path obtained, in taking its antithetic path — that is given a path ${\displaystyle \{\varepsilon _{1},\dots ,\varepsilon _{M}\))$ to also take ${\displaystyle \{-\varepsilon _{1},\dots ,-\varepsilon _{M}\))$. The advantage of this technique is twofold: it reduces the number of normal samples to be taken to generate N paths, and it reduces the variance of the sample paths, improving the precision.

Suppose that we would like to estimate

${\displaystyle \theta =\mathrm {E} (h(X))=\mathrm {E} (Y)\,}$

For that we have generated two samples

${\displaystyle Y_{1}{\text{ and ))Y_{2}\,}$

An unbiased estimate of ${\displaystyle {\theta ))$ is given by

${\displaystyle {\hat {\theta ))={\frac {Y_{1}+Y_{2)){2)).}$

And

${\displaystyle {\text{Var))({\hat {\theta )))={\frac ((\text{Var))(Y_{1})+{\text{Var))(Y_{2})+2{\text{Cov))(Y_{1},Y_{2})}{4))}$

so variance is reduced if ${\displaystyle {\text{Cov))(Y_{1},Y_{2})}$ is negative.

Example 1

If the law of the variable X follows a uniform distribution along [0, 1], the first sample will be ${\displaystyle u_{1},\ldots ,u_{n))$, where, for any given i, ${\displaystyle u_{i))$ is obtained from U(0, 1). The second sample is built from ${\displaystyle u'_{1},\ldots ,u'_{n))$, where, for any given i: ${\displaystyle u'_{i}=1-u_{i))$. If the set ${\displaystyle u_{i))$ is uniform along [0, 1], so are ${\displaystyle u'_{i))$. Furthermore, covariance is negative, allowing for initial variance reduction.

Example 2: integral calculation

We would like to estimate

${\displaystyle I=\int _{0}^{1}{\frac {1}{1+x))\,\mathrm {d} x.}$

The exact result is ${\displaystyle I=\ln 2\approx 0.69314718}$. This integral can be seen as the expected value of ${\displaystyle f(U)}$, where

${\displaystyle f(x)={\frac {1}{1+x))}$

and U follows a uniform distribution [0, 1].

The following table compares the classical Monte Carlo estimate (sample size: 2n, where n = 1500) to the antithetic variates estimate (sample size: n, completed with the transformed sample 1 − u_i):

	Estimate	standard error
Classical Estimate	0.69365	0.00255
Antithetic Variates	0.69399	0.00063

The use of the antithetic variates method to estimate the result shows an important variance reduction.