Is a minimal sufficient statistic also a complete statistic I know that if a statistic is both sufficient and complete then it must also be minimal sufficient.
But on the other hand, could I say a minimal sufficient statistic must also be a complete statistic?  
 A: Examples of minimal sufficient statistic which are not complete are aplenty.
A simple instance is $X\sim  U (\theta,\theta+1)$ where $\theta\in \mathbb R$.
It is not difficult to show $X$ is a minimal sufficient statistic for $\theta$. However, $$E_{\theta}(\sin 2\pi X)=\int_{\theta}^{\theta+1} \sin (2\pi x)\,\mathrm{d}x=0\quad,\forall\,\theta$$
And $\sin 2\pi X$ is not identically zero almost everywhere, so that $X$ is not a complete statistic.
Another example for discrete distribution can be found in textbooks as an exercise or otherwise:
Let $X$ have the mass function
$$f_{\theta}(x)=\begin{cases}\theta&,\text{ if }x=-1\\\theta^x(1-\theta)^2&,\text{ if }x=0,1,2,\ldots\end{cases}\quad,\,\theta\in (0,1)$$
It can be verified that $X$ is minimal sufficient for $\theta$.
Suppose $\psi$ is any measurable function of $X$. Then
\begin{align}
&\qquad\quad E_{\theta}(\psi(X))=0\quad,\forall\,\theta
\\&\implies \theta\psi(-1)+\sum_{x=0}^\infty \psi(x)\theta^x(1-\theta)^2=0\quad,\forall\,\theta
\\&\implies \sum_{x=0}^\infty \psi(x)\theta^x=\frac{-\theta\psi(-1)}{(1-\theta)^2}=-\sum_{x=0}^\infty\psi(-1)x\theta^x\quad,\forall\,\theta
\end{align}
Comparing coefficient of $\theta^x$ for $x=0,1,2,\ldots$ we have $$\psi(x)=-x\psi(-1)\quad,\, x=0,1,2,\ldots$$
If $\psi(-1)=c\ne 0$, then $$\psi(x)=-cx\quad,\, x=0,1,2,\ldots$$
That is, $\psi$ is non-zero with positive probability. Hence $X$ is not complete for $\theta$.
A: Consider $N(\theta,\theta)$ where $\theta>0$.Of course $\dfrac{1}{n}\sum_{i=1}^n X_i$ is minimal sufficient but not complete. To see why it is not complete, find $a$ and $b$ such that:
$$E\Big(a\sum_{i=1}^n (X_i-\overline{X})^2 \Big)=E\Big(b\sum_{i=1}^nX_i^2\Big)=\theta^2$$
and therefore $E\Big(a\sum_{i=1}^n (X_i-\overline{X})^2-b\sum_{i=1}^nX_i^2\Big)=0$ for all $\theta$.
A: In the Cauchy distribution with unknown location,
$$f(x;\mu) = \frac{1}{\pi} \, \frac{1}{1+(x-\mu)^2}$$
for a sample $(X_1,\ldots,X_n)$
the order statistic $(X_{(1)},\ldots,X_{(n)})$ is minimal sufficient, but it is incomplete since $$\mathbb{E}_\mu[\phi(X_{(i)} - X_{(j)})]\qquad i\ne j$$is constant in $\mu$ for bounded functions $\phi$. Or since 
$$\mathbb{E}_\mu[\phi(X_{(i)} - X_{(j)})]\qquad 1< i\ne j <n$$is (well-defined and) constant in $\mu$.
