# Stein's lemma

Stein's lemma, named in honor of Charles Stein, is a theorem of probability theory that is of interest primarily because of its applications to statistical inference — in particular, to James–Stein estimation and empirical Bayes methods — and its applications to portfolio choice theory. The theorem gives a formula for the covariance of one random variable with the value of a function of another, when the two random variables are jointly normally distributed.

## Statement of the lemma

Suppose X is a normally distributed random variable with expectation μ and variance σ2. Further suppose g is a function for which the two expectations E(g(X) (X − μ)) and E(g ′(X)) both exist. (The existence of the expectation of any random variable is equivalent to the finiteness of the expectation of its absolute value.) Then

$E{\bigl (}g(X)(X-\mu ){\bigr )}=\sigma ^{2}E{\bigl (}g'(X){\bigr )}.$ In general, suppose X and Y are jointly normally distributed. Then

$\operatorname {Cov} (g(X),Y)=\operatorname {Cov} (X,Y)E(g'(X)).$ ## Proof

The univariate probability density function for the univariate normal distribution with expectation 0 and variance 1 is

$\varphi (x)={1 \over {\sqrt {2\pi }}}e^{-x^{2}/2}$ Since $\int x\exp(-x^{2}/2)=-\exp(-x^{2}/2)$ we get from integration by parts:

$E[g(X)X]={\frac {1}{\sqrt {2\pi }}}\int g(x)x\exp(-x^{2}/2)dx={\frac {1}{\sqrt {2\pi }}}\int g'(x)\exp(-x^{2}/2)dx=E[g'(X)]$ .

The case of general variance $\sigma ^{2}$ follows by substitution.

## More general statement

Suppose X is in an exponential family, that is, X has the density

$f_{\eta }(x)=\exp(\eta 'T(x)-\Psi (\eta ))h(x).$ Suppose this density has support $(a,b)$ where $a,b$ could be $-\infty ,\infty$ and as $x\rightarrow a{\text{ or }}b$ , $\exp(\eta 'T(x))h(x)g(x)\rightarrow 0$ where $g$ is any differentiable function such that $E|g'(X)|<\infty$ or $\exp(\eta 'T(x))h(x)\rightarrow 0$ if $a,b$ finite. Then

$E((h'(X)/h(X)+\sum \eta _{i}T_{i}'(X))g(X))=-Eg'(X).$ The derivation is same as the special case, namely, integration by parts.

If we only know $X$ has support $\mathbb {R}$ , then it could be the case that $E|g(X)|<\infty {\text{ and }}E|g'(X)|<\infty$ but $\lim _{x\rightarrow \infty }f_{\eta }(x)g(x)\not =0$ . To see this, simply put $g(x)=1$ and $f_{\eta }(x)$ with infinitely spikes towards infinity but still integrable. One such example could be adapted from $f(x)={\begin{cases}1&x\in [n,n+2^{-n})\\0&{\text{otherwise}}\end{cases}}$ so that $f$ is smooth.

Extensions to elliptically-contoured distributions also exist.