ex. x_i \sim \exp(\lambda)
\kern{10pt} f_{X_i}(x) = \lambda e^{-\lambda x}~I(x>0)
\\[10pt]
\mu = {1\over\lambda}_,
\kern{10pt}
\sigma^2 = {1\over\lambda^2}
By CLT,
\sqrt{n}~\bigg(\overline{X}-{1\over n}\bigg) \sim N\bigg(0_,~~\frac{1}{\lambda^2}\bigg)
\begin{align*}
1-\alpha &= P\Big(a < \hat\lambda < b\Big)
= P\bigg(a<{1\over\overline{X}}<b\bigg)
\\[15pt]
&= P\bigg({1\over b}<\overline{X}
<{1\over a}\bigg)
\\[15pt]
&=P\bigg(
\dfrac{{1\over b}-{1 \over\lambda}}
{{1\over\lambda}
\cdot{1\over\sqrt{n}}}<
\dfrac{{\overline{X}}-{1 \over\lambda}}
{{1\over\lambda}
\cdot{1\over\sqrt{n}}}
<\dfrac{{1\over a}-{1 \over\lambda}}
{{1\over\lambda}
\cdot{1\over\sqrt{n}}}\bigg)
\\[20pt]
&\kern{5pt}if.~n \to \infty
\\[5pt]
&=P\bigg(
\dfrac{{1\over b}-{1 \over\lambda}}
{{1\over\lambda}
\cdot{1\over\sqrt{n}}}<
Z
<\dfrac{{1\over a}-{1 \over\lambda}}
{{1\over\lambda}
\cdot{1\over\sqrt{n}}}\bigg)
\end{align*}
\begin{align*}
P(a<\hat\lambda<b) &\backsimeq P\Big(
-Z_{a\over2}<\sqrt{n}~\lambda~\big(\overline{X}-\textstyle{1\over \lambda}\big)<
Z_{a\over2}
\Big)
\\[10pt]
&= P\Big(
-Z_{a\over2}<\sqrt{n}~\big(\lambda\overline{X}-1\big)<
Z_{a\over2}
\Big)
\end{align*}
다시 정리해보면 다음과 같다.
P\bigg(1-{Z_{a\over2}\over \sqrt{n}}
< \lambda\overline{X} <
1+{Z_{a\over2}\over \sqrt{n}} \bigg)
P\Bigg({1\over \overline{X}} -{Z_{a\over2}\over \overline{X}\sqrt{n}}
< \lambda <
{1\over \overline{X}} +{Z_{a\over2}\over \overline{X}\sqrt{n}}
\Bigg)
\therefore~\lambda의 100(1-\alpha)% CI C.I.
{1\over \overline{X}}~-~
Z_{a\over2} \cdot
{1\over \overline{X}\sqrt{n}}
\kern{15pt}
\longrightarrow
\kern{15pt}
\hat\lambda~\pm~
Z_{a\over2} \cdot
{\lambda \over \sqrt{n}}
여기서 \lambda / \sqrt{n}을 추정량의 표준오차(s.e.)라고 한다.