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.

The delta method was derived from propagation of error, and the idea behind was known in the early 20th century.^{[1]} Its statistical application can be traced as far back as 1928 by T. L. Kelley.^{[2]} A formal description of the method was presented by J. L. Doob in 1935.^{[3]} Robert Dorfman also described a version of it in 1938.^{[4]}

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 X_{n} satisfying

where *θ* and *σ*^{2} 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.

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 X_{n} 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.

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.

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 scalar-valued 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,

Suppose *X _{n}* is binomial with parameters and

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 *X _{n}* 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.

The delta method is often used in a form that is essentially identical to that above, but without the assumption that X_{n} 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:^{[5]}

where h_{r} is the *r*th element of *h*(*B*) and *B _{i}* is the

When *g′*(*θ*) = 0 the delta method cannot be applied. However, if *g′′*(*θ*) exists and is not zero, the second-order delta method can be applied. By the Taylor expansion, , so that the variance of relies on up to the 4th moment of .

The second-order delta method is also useful in conducting a more accurate approximation of 's distribution when sample size is small. . For example, when follows the standard normal distribution, can be approximated as the weighted sum of a standard normal and a chi-square with degree-of-freedom of 1.

A version of the delta method exists in nonparametric statistics. Let be an independent and identically distributed random variable with a sample of size with an empirical distribution function , and let be a functional. If is Hadamard differentiable with respect to the Chebyshev metric, then

where and , with denoting the empirical influence function for . A nonparametric pointwise asymptotic confidence interval for is therefore given by

where denotes the -quantile of the standard normal. See Wasserman (2006) p. 19f. for details and examples.