Rao–Blackwell theorem

• An estimator δ(X) is an observable random variable (i.e. a statistic) used for estimating some unobservable quantity. For example, one may be unable to observe the average height of all male students at the University of X, but one may observe the heights of a random sample of 40 of them. The average height of those 40—the "sample average"—may be used as an estimator of the unobservable "population average".
• A sufficient statistic T(X) is a statistic calculated from data X to estimate some parameter θ for which no other statistic which can be calculated from data X provides any additional information about θ. It is defined as an observable random variable such that the conditional probability distribution of all observable data X given T(X) does not depend on the unobservable parameter θ, such as the mean or standard deviation of the whole population from which the data X was taken. In the most frequently cited examples, the "unobservable" quantities are parameters that parametrize a known family of probability distributions according to which the data are distributed.

In other words, a sufficient statistic T(X) for a parameter θ is a statistic such that the conditional distribution of the data X, given T(X), does not depend on the parameter θ.

• A Rao–Blackwell estimator δ₁(X) of an unobservable quantity θ is the conditional expected value E(δ(X) | T(X)) of some estimator δ(X) given a sufficient statistic T(X). Call δ(X) the "original estimator" and δ₁(X) the "improved estimator". It is important that the improved estimator be observable, i.e. that it does not depend on θ. Generally, the conditional expected value of one function of these data given another function of these data does depend on θ, but the very definition of sufficiency given above entails that this one does not.
• The mean squared error of an estimator is the expected value of the square of its deviation from the unobservable quantity being estimated.

The improved estimator is unbiased if and only if the original estimator is unbiased, as may be seen at once by using the law of total expectation. The theorem holds regardless of whether biased or unbiased estimators are used.

The theorem seems very weak: it says only that the Rao–Blackwell estimator is no worse than the original estimator. In practice, however, the improvement is often enormous.

Phone calls arrive at a switchboard according to a Poisson process at an average rate of λ per minute. This rate is not observable, but the numbers X₁, ..., X_n of phone calls that arrived during n successive one-minute periods are observed. It is desired to estimate the probability e^−λ that the next one-minute period passes with no phone calls.

An extremely crude estimator of the desired probability is

${\displaystyle \delta _{0}=\left\{{\begin{matrix}1&{\text{if}}\ X_{1}=0,\\0&{\text{otherwise,}}\end{matrix}}\right.}$

i.e., it estimates this probability to be 1 if no phone calls arrived in the first minute and zero otherwise. Despite the apparent limitations of this estimator, the result given by its Rao–Blackwellization is a very good estimator.

The sum

${\displaystyle S_{n}=\sum _{i=1}^{n}X_{i}=X_{1}+\cdots +X_{n}}$

can be readily shown to be a sufficient statistic for λ, i.e., the conditional distribution of the data X₁, ..., X_n, depends on λ only through this sum. Therefore, we find the Rao–Blackwell estimator

${\displaystyle \delta _{1}=\operatorname {E} (\delta _{0}\mid S_{n}=s_{n}).}$

After doing some algebra we have

${\displaystyle {\begin{aligned}\delta _{1}&=\operatorname {E} \left(\mathbf {1} _{\{X_{1}=0\}}{\Bigg |}\sum _{i=1}^{n}X_{i}=s_{n}\right)\\&=P\left(X_{1}=0{\Bigg |}\sum _{i=1}^{n}X_{i}=s_{n}\right)\\&=P\left(X_{1}=0,\sum _{i=2}^{n}X_{i}=s_{n}\right)\times P\left(\sum _{i=1}^{n}X_{i}=s_{n}\right)^{-1}\\&=e^{-\lambda }{\frac {\left((n-1)\lambda \right)^{s_{n}}e^{-(n-1)\lambda }}{s_{n}!}}\times \left({\frac {(n\lambda )^{s_{n}}e^{-n\lambda }}{s_{n}!}}\right)^{-1}\\&={\frac {\left((n-1)\lambda \right)^{s_{n}}e^{-n\lambda }}{s_{n}!}}\times {\frac {s_{n}!}{(n\lambda )^{s_{n}}e^{-n\lambda }}}\\&=\left(1-{\frac {1}{n}}\right)^{s_{n}}\end{aligned}}}$

Since the average number of calls arriving during the first n minutes is nλ, one might not be surprised if this estimator has a fairly high probability (if n is big) of being close to

${\displaystyle \left(1-{1 \over n}\right)^{n\lambda }\approx e^{-\lambda }.}$

So δ₁ is clearly a very much improved estimator of that last quantity. In fact, since S_n is complete and δ₀ is unbiased, δ₁ is the unique minimum variance unbiased estimator by the Lehmann–Scheffé theorem.

Rao–Blackwellization is an idempotent operation. Using it to improve the already improved estimator does not obtain a further improvement, but merely returns as its output the same improved estimator.

If the conditioning statistic is both complete and sufficient, and the starting estimator is unbiased, then the Rao–Blackwell estimator is the unique "best unbiased estimator": see Lehmann–Scheffé theorem.

An example of an improvable Rao–Blackwell improvement, when using a minimal sufficient statistic that is not complete, was provided by Galili and Meilijson in 2016.[5] Let ${\displaystyle X_{1},\ldots ,X_{n}}$ be a random sample from a scale-uniform distribution ${\displaystyle X\sim U\left((1-k)\theta ,(1+k)\theta \right),}$ with unknown mean ${\displaystyle E[X]=\theta }$ and known design parameter ${\displaystyle k\in (0,1)}$. In the search for "best" possible unbiased estimators for ${\displaystyle \theta ,}$ it is natural to consider ${\displaystyle X_{1}}$ as an initial (crude) unbiased estimator for ${\displaystyle \theta }$ and then try to improve it. Since ${\displaystyle X_{1}}$ is not a function of ${\displaystyle T=\left(X_{(1)},X_{(n)}\right)}$, the minimal sufficient statistic for ${\displaystyle \theta }$ (where ${\displaystyle X_{(1)}=\min(X_{i})}$ and ${\displaystyle X_{(n)}=\max(X_{i})}$), it may be improved using the Rao–Blackwell theorem as follows:

${\displaystyle {\hat {\theta }}_{RB}=E_{\theta }\left[X_{1}|X_{(1)},X_{(n)}\right]={\frac {X_{(1)}+X_{(n)}}{2}}.}$

However, the following unbiased estimator can be shown to have lower variance:

${\displaystyle {\hat {\theta }}_{LV}={\frac {1}{2\left(k^{2}{\frac {n-1}{n+1}}+1\right)}}\left[(1-k){{X}_{(1)}}+(1+k){{X}_{(n)}}\right].}$

And in fact, it could be even further improved when using the following estimator:

${\displaystyle {\hat {\theta }}_{BAYES}={\frac {n+1}{n}}\left[1-{\frac {{\frac {\left({\frac {{X}_{(1)}}{1-k}}\right)}{\left({\frac {{X}_{(n)}}{1+k}}\right)}}-1}{{{\left[{\frac {\left({\frac {{X}_{(1)}}{1-k}}\right)}{\left({\frac {{X}_{(n)}}{1+k}}\right)}}\right]}^{n+1}}-1}}\right]{\frac {X_{(n)}}{1+k}}}$

• Basu's theorem — Another result on complete sufficient and ancillary statistics
• C. R. Rao
• David Blackwell

• Nikulin, M.S. (2001) [1994], "Rao–Blackwell–Kolmogorov theorem", Encyclopedia of Mathematics, EMS Press