Delta method

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In statistics, the delta method is a result concerning the approximate probability distribution for a function of an asymptotically normal statistical estimator from knowledge of the limiting variance of that estimator.

Univariate delta method[edit]

While the delta method generalizes easily to a multivariate setting, careful motivation of the technique is more easily demonstrated in univariate terms. Roughly, if there is a sequence of random variables Xn satisfying

where θ and σ2 are finite valued constants and denotes convergence in distribution, then

for any function g satisfying the property that g′(θ) exists and is non-zero valued.

Proof in the univariate case[edit]

Demonstration of this result is fairly straightforward under the assumption that g′(θ) is continuous. To begin, we use the mean value theorem (i.e.: the first order approximation of a Taylor series using Taylor's theorem):

where lies between Xn and θ. Note that since and , it must be that and since g′(θ) is continuous, applying the continuous mapping theorem yields

where denotes convergence in probability.

Rearranging the terms and multiplying by gives

Since

by assumption, it follows immediately from appeal to Slutsky's Theorem that

This concludes the proof.

Proof with an explicit order of approximation[edit]

Alternatively, one can add one more step at the end, to obtain the order of approximation:

This suggests that the error in the approximation converges to 0 in probability.

Multivariate delta method[edit]

By definition, a consistent estimator B converges in probability to its true value β, and often a central limit theorem can be applied to obtain asymptotic normality:

where n is the number of observations and Σ is a (symmetric positive semi-definite) covariance matrix. Suppose we want to estimate the variance of a function h of the estimator B. Keeping only the first two terms of the Taylor series, and using vector notation for the gradient, we can estimate h(B) as

which implies the variance of h(B) is approximately

One can use the mean value theorem (for real-valued functions of many variables) to see that this does not rely on taking first order approximation.

The delta method therefore implies that

or in univariate terms,

Example[edit]

Suppose Xn is Binomial with parameters and n. Since

we can apply the Delta method with g(θ) = log(θ) to see

Hence, even though for any finite n, the variance of does not actually exist (since Xn can be zero), the asymptotic variance of does exist and is equal to

Note that since p>0, as , so with probability converging to one, is finite for large n.

Moreover, if and are estimates of different group rates from independent samples of sizes n and m respectively, then the logarithm of the estimated relative risk has asymptotic variance equal to

This is useful to construct a hypothesis test or to make a confidence interval for the relative risk.

Note[edit]

The delta method is often used in a form that is essentially identical to that above, but without the assumption that Xn or B is asymptotically normal. Often the only context is that the variance is "small". The results then just give approximations to the means and covariances of the transformed quantities. For example, the formulae presented in Klein (1953, p. 258) are:

where hr is the rth element of h(B) and Biis the ith element of B. The only difference is that Klein stated these as identities, whereas they are actually approximations.

See also[edit]

References[edit]