Lehmann–Scheffé theorem

Let ${\displaystyle {\vec {X}}=X_{1},X_{2},\dots ,X_{n}}$ be a random sample from a distribution that has p.d.f (or p.m.f in the discrete case) ${\displaystyle f(x:\theta )}$ where ${\displaystyle \theta \in \Omega }$ is a parameter in the parameter space. Suppose ${\displaystyle Y=u({\vec {X}})}$ is a sufficient statistic for θ, and let ${\displaystyle \{f_{Y}(y:\theta ):\theta \in \Omega \}}$ be a complete family. If ${\displaystyle \varphi$ :\operatorname {E} [\varphi (Y)]=\theta } then ${\displaystyle \varphi (Y)}$ is the unique MVUE of θ.

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.[4] Let ${\displaystyle X_{1},\ldots ,X_{n}}$ be a random sample from a scale-uniform distribution ${\displaystyle X\sim U((1-k)\theta ,(1+k)\theta ),}$ with unknown mean ${\displaystyle \operatorname {E} [X]=\theta }$ 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 ${\displaystyle X_{(1)}=\min _{i}X_{i}}$ and ${\displaystyle X_{(n)}=\max _{i}X_{i}}$), it may be improved using the Rao–Blackwell theorem as follows:

${\displaystyle {\hat {\theta }}_{RB}=\operatorname {E} _{\theta }[X_{1}\mid X_{(1)},X_{(n)}]={\frac {X_{(1)}+X_{(n)}}{2}}.}$

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

${\displaystyle {\hat {\theta }}_{LV}={\frac {1}{k^{2}{\frac {n-1}{n+1}}+1}}\cdot {\frac {(1-k)X_{(1)}+(1+k)X_{(n)}}{2}}.}$

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

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

The model is a scale model. Optimal equivariant estimators can then be derived for loss functions that are invariant.[5]

• Basu's theorem
• Complete class theorem
• Rao–Blackwell theorem