# Some questions about confidence intervals, quantiles and cdf/pdf

My question is related to : Maximum likelihood estimation and the n-th order statistic estimation-and-the-n-th-order-statistic. I will try to answer the questions, and I would like to ask you guys to look at it, and criticize it, because I want all the answers to be correct obviously :)

Given $$f_{\theta}(x)=e^{ (\theta -x)}$$for $x \ge \theta$, otherwise $f_\theta = 0$

I showed that the MLE $\hat \theta_n$of $\theta= min(x_1,x_2, ... x_n) = X_{(1)}$

a.) What is the cdf of $\hat \theta_n$

b.) What is the pdf of $\hat \theta_n$ and draw the distribution

c.) We take a sample of size $n$ from this distribution and by chance we took a very unusual value $\hat \theta_n = q_{1-\alpha}$ where $q_{1-\alpha}$ is the $1-\alpha$ quantile of the distribution of $\hat\theta_n$. Determine $q_{1-\alpha}$ as a function of of $\theta$ and $\alpha$

d.) Again we take sample of size $n$ and now we got very lucky by obtaining $\hat\theta_n=q_0$ where $q_0$ is the 0th quantile of the $\hat \theta_n$ distribution. Determine $q_o$

e.)We observe $X_1=3, X_2=4, X_3=10$. With the answers of c.) and d.) we should now be able to obtain a 95%-confidence interval for $\theta$

My try:

a.) the cdf is $F_{\hat\theta_n}1-(1-(F_{\theta}(x))^n= 1-e^{n\theta - nx}$

b.) $f_{\hat\theta_n}= ne^{n\theta - nx} = \frac{d}{dx}( 1-e^{n\theta - nx})$.

c.) $$1-\alpha =F(q_{1-\alpha})= 1-e^{n\theta -nq_{1-\alpha}} \implies \ln{(1-\alpha)}=n\theta-nq_{1-\alpha} \implies -nq_{1-\alpha}= \ln{(1-\alpha)}-n\theta \implies q_{1-\alpha}=-\frac{\ln{(1-\alpha)}}{n} +\theta$$

d.)$$0=F(q_0)= 1- e^{n\theta -nq_0} \implies 1=e^{n(\theta-q_0)} \implies q_0=\theta$$

e.) We need a $1-\alpha$-confidence interval for $\theta$. Let $c_l$ and $c_u$ denote the lower and upper boundaries of the confidence interval of $\theta$. I will solve $$P(c_l < \hat\theta_n < c_r)= 1-\alpha$$ by requiring $$P(\hat\theta_n \le c_l) =P(\hat\theta_n \ge c_r)= \frac{1}2{}\alpha$$ Now I find the following equations

$$1-e^{n\theta-nc_l} = \frac{\alpha}{2}\implies \ln(1-\frac{1}{2}\alpha)=n\theta-nc_l \implies c_l = -\frac{\ln(1-\frac{1}{2}\alpha)}{n}+\theta$$

Similarly

$$e^{n\theta-nc_u}= \frac{1}{2} \alpha \implies c_u=-\frac{\ln(\frac{1}{2}\alpha)}{n}+\theta$$

Now I have found that, whatever the value of $\theta$:

$$P(-\frac{\ln(1-\frac{1}{2}\alpha)}{n}+\theta <\hat\theta_n <-\frac{\ln(\frac{1}{2}\alpha)}{n}+\theta ) = 1-\alpha$$ By re-arranging the inequalities, we see this is equivalent to

$$P(\frac{\ln(\frac{1}{2}\alpha)}{n}+\hat\theta_n <\theta < \frac{\ln(1-\frac{1}{2}\alpha)}{n}+\hat\theta_n ) = 1-\alpha$$

Therefore, a $100(1-\alpha)$% confidende interval for $\theta$ is given by: $$\left( \frac{\ln(\frac{1}{2}\alpha)}{n}+\hat\theta_n <\theta < \frac{\ln(1-\frac{1}{2}\alpha)}{n}+\hat\theta_n \right)$$

Looking at the observation, we have min(X1, X2, X3)= 3 and n = 3. So our 95% confidence interval should be: $$\left( \frac{\ln(\frac{0.05}{2})}{3}+3 <\theta < \frac{\ln(1-\frac{0.05}{2})}{3}+3 \right)$$This interval is [1.77, 2.99]

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Strictly speaking, (e) doesn't ask you to do anything. It simply makes a comment about what should be possible (whoever wrote that question needs a jolly good spanking and no supper). Let's assume the writer of it wants you to obtain the 95% CI. Could you write down the distribution of a quantity such as, say $\theta/\hat{\theta}$ or $\hat{\theta}/\theta$? –  Glen_b Oct 24 '12 at 21:27
@Glen_b: The original question was: Use the answers in c.) and d.) to obtain a 95%-confidence interval for $\theta$. As for your question, I don't know exactly what you want me to do :-). I am familiar with confidence intervals to estimate means (given that the sample mean is normally distributed) etc., but I don't know exactly what I have to do. Can you check the other answers as well ? I'm not sure about those. For example, I would like to know how to graph the distribution for $\hat \theta_n$ –  MSKfdaswplwq Oct 24 '12 at 21:34
(i) The problem is that the distribution for $\hat{\theta}$ depends on $\theta$. If you look at the distribution of $Q = \hat{\theta}/\theta$, it doesn't depend on $\theta$ (you can construct an interval for it that doesn't depend on $\theta$). From that interval you can back out an interval for $\theta$. $Q$ is called a pivotal quantity. (ii) You don't know how to graph an exponential distribution? I'm not even sure what the difficulty is here - it's a function of $x$, which you have already given (I didn't check your work though). –  Glen_b Oct 25 '12 at 1:05
I think I got it now. –  MSKfdaswplwq Oct 25 '12 at 23:37