The p.d.f of an exponential distribution is,
f (x|β) = βexp(-βx)
As given in exercise 11 of section 8.8, with d(x) = x
Therefore \begin{aligned}T=\sum_{i-1}^{n}X{i}end{aligned}
Know that,
E(Xi) = 1/β and Var (Xi) = 1/β2
Hence,
\begin{aligned}E(T)=E\left(\sum_{i-1}^{n}\right)=\sum_{i-1}^{n}E(X_{i})=\frac{n}{\beta}\end{aligned}
\begin{aligned}Var(T)=Var\left(\sum_{i-1}^{n}X_{i}\right)\end{aligned}
\begin{aligned}Var\left(\sum_{i-1}^{n}X_{i}\right)\end{aligned}
=n/β2
Since any linear function of T will also be an efficient estimator, it follows that x̄n =T/n will be an efficient estimator of 1/β
So,
E(x̄n) = E(T/n)
= n/nβ
= 1/β
Var (x̄n) = Var (T/n)
= 1/n2 Var (T)
= n/n2 β
= 1/nβ
Therefore, the mean of the estimator is 1/β and the variance of the estimator is 1/nβ.