Find a Confidence Interval for Data from a Uniform Distribution on $(\theta-.5,\theta+.5).$

Let $$X_1,...,X_n$$ be a random sample on $$\text{Uniform}(\theta -1/2, \theta +1/2).$$ I need to find a confidence interval for $$\theta$$ with ($$1-\alpha$$) of confidence.

I have this:

$$\max(X_i)-1/2<\theta<\min(X_i) +1/2,$$ hence I say $$\mathbb{P}(\max (X_i)-1/2<\theta<\min(X_i) +1/2)\ge 1-\alpha$$

or using the statistics $$\min(X_i) , \max(X_i),$$

$$\mathbb{P}(\min(X_i) \le\theta\le\max(X_i))\ge 1-\alpha$$
$$\mathbb{P}(\theta\le \max(X_i)) -\mathbb{P}(\min(X_i) \le\theta)$$ $$\ge 1-\alpha.$$

• The interval that you are proposing does not depend on $\alpha$ Jun 14 '21 at 20:49
• A continuation of math.stackexchange.com/questions/4172924/… Jun 14 '21 at 20:50
• math.stackexchange.com/q/3230142/321264 Jun 15 '21 at 13:22
• You are correct to use $\min(X_i),\max(X_i).$ What remains is to find the distributions of the min and the max (related to beta distributions). Jun 15 '21 at 21:18
• Use $(X_{(1)}+X_{(n)})/2-\theta$ is a pivotal quantity that you can use to construct the interval. See stats.stackexchange.com/questions/352854/… for a similar approach. Jun 16 '21 at 18:05

Definitely not the most efficient approach (doesn't condition on the sufficient statistic), but the mean is unbiased for $$\theta$$. And the variance of a single observation from the above distribution is 1/12. So, by the CLT:

$$\sqrt{n} \left( \bar{X} - \theta \right) \rightarrow_d \mathcal{N}\left(0, 1/12 \right)$$

Which means that a $$1-\alpha$$ confidence interval for $$\theta$$ can be given by

$$\left(\bar{X}+(12n)^{-.5}\mathcal{Z}_{\alpha/2}, \bar{X}+(12n)^{-.5} \mathcal{Z}_{1-\alpha/2} \right)$$

• Right about 'not the most efficient'. For $n = 5, 20$ your CIs are of widths $0.506, 0.253,$ respectively; while CIs based on max and min have average widths $0.451, 0.139,$ respectively. Jun 15 '21 at 21:17
• @BruceET well using the min and max would be biased. Otherwise, every instance of the German Tank problem would claim you have the last tank that was made. Jun 15 '21 at 22:03
• Not advocating use of max or min, but of midrange (their average). So if a sample of size 12 has midrange 9.95, the 95% CI would be approx (9.89, 10.02) of length 0.14. Jun 15 '21 at 23:08
• thanks that was so helpfull Jun 16 '21 at 0:25

I too will avoid analytic derivations of distributions of $$\max(X_i)$$ and $$\min(X_i)$$ because I guess that is the main point of this assignment. [Also see this page.]

However, results from a simulation for the case $$n = 20, \theta = 10,$$ based on the sample midrange, are shown below.

set.seed(2021)
n=20
mr = replicate(10^6, mean(range(runif(n, 9.5, 10.5))))
CI = quantile(mr, c(.025,.975)); CI
2.5%     97.5%
9.930509 10.069571

hdr = "Simulated Distributions of Midrange"
hist(mr, prob=T, br=50, col="skyblue2", main=hdr)
abline(v = CI, col="red", lwd=2, lty="dashed") Frequentist interval via pivotal quantity: An improvement over the interval suggested by @AdamO but still suboptimal solution can be obtained using almost exactly the same method as the one I give here so I omit the details of the following derivation. The pdf of $$Z=\frac{X_{(1)}+X_{(n)}}2-\theta,$$ is $$f(z)=n(1-2|z|)^{n-1}$$ for $$-1/2 \le z \le 1/2$$. Since the distribution of $$Z$$ doesn't depend on $$\theta$$, $$Z$$ is a pivotal quantity.

This pdf is symmetric and the upper $$\alpha/2$$-quantile of $$Z$$ is $$\frac{1-\alpha^{1/n}}2$$. Thus $$P\left(-\frac{1-\alpha^{1/n}}2<\frac{X_{(1)}+X_{(n)}}2-\theta<\frac{1-\alpha^{1/n}}2\right)=1-\alpha.$$ Inverting the double inequality, we find that $$\frac{X_{(1)}+X_{(n)}}2 \pm \frac{1-\alpha^{1/n}}2 \tag{1}$$
is a $$1-\alpha$$ confidence interval for $$\theta$$. The midrange $$(X_{(1)}+X_{(n)})/2$$ is not a sufficient statistics for $$\theta$$, however.

Whittinghill and Hogg: As pointed out by @COOLSerdash, by inverting a likelihood ratio statistic these authors derive the interval $$\left(x_{(n)}-\frac{(1-\alpha)^{1/n}}2,x_{(1)}+\frac{(1-\alpha)^{1/n}}2\right)\tag{2}$$ which is a function of the sufficient statistic $$(X_{(1)},X_{(2)})$$ for $$\theta$$. However, simulations (see below) suggest that this interval is also suboptimal.

A Bayesian credible interval: An alternative is to represent our prior ignorance about $$\theta$$ by a uniform improper prior $$\pi(\theta)=1$$. The posterior density of $$\theta$$ is then $$\pi(\theta|\mathbf{x})\propto \prod_{i=1}^n I_{(\theta-\frac12,\theta+\frac12)}(x_i)=I_{(x_{(n)}-\frac12,x_{(1)}+\frac12)}(\theta),$$ that is, conditional on the observations, $$\theta$$ is uniformly distributed on the interval from $$(x_{(n)}-\frac12,x_{(1)}+\frac12)$$. A $$1-\alpha$$ credible interval for $$\theta$$ is therefore $$\left(x_{(n)}-\frac12 + \frac{\alpha}2L, x_{(1)}+\frac12 - \frac{\alpha}2L\right) \tag{3}$$ where $$L=1-(x_{(n)}-x_{(1)})$$. Interestingly, judged by frequentist criteria, based on the following simulation, this interval appear to have the exact nominal coverage but is considerably shorter on average than both (1) and (2):

ci.normal <- function(x, alpha) {
n <- length(x)
mean(x) + c(-1,1)*(12*n)^(-.5)*qnorm(alpha/2, lower.tail = FALSE)
}
ci.pivot <- function(x, alpha=.05) {
n <- length(x)
(min(x)+max(x))/2 + c(-1,1)*(1 - alpha^(1/n))/2
}
ci.wh <- function(x, alpha) {
n <- length(x)
c <- (1-alpha)^(1/n)/2
c(max(x)-c, min(x)+c)
}
ci.bayes <- function(x, alpha=.05) {
L <- 1 - (max(x)-min(x))
c(max(x) - .5 + L*alpha/2, min(x) + .5 - L*alpha/2)
}
coverage <- function(fn, theta=0, nsim=100000, n, alpha=0.05) {
hits <- 0
ci.lengths <- numeric(nsim)
for (i in 1:nsim) {
x <- runif(n, theta-.5, theta+.5)
ci <- fn(x,alpha)
ci.lengths[i] <- ci - ci
if (ci < theta & ci > theta)
hits <- hits + 1
}
list(coverage = hits/nsim, meanlength = mean(ci.lengths))
}
> coverage(ci.normal, n=5)
$coverage  0.95315$meanlength
 0.5060605

> coverage(ci.pivot, n=5)
$coverage  0.95004$meanlength
 0.4507197

> coverage(ci.wh, n=5)
$coverage  0.94968$meanlength
 0.3226174

> coverage(ci.bayes, n=5)
$coverage  0.94991$meanlength
 0.3169024
• The confidence interval $(Y_n - c, Y_1 + c)$ with $c=(1/2)(1-\alpha)^{1/n}$ has similar length and coverage as the Bayesian credible interval. It still seems to be a bit wider though. Jun 18 '21 at 7:37
• @COOLSerdash Indeed it does. How did you derive this interval? Jun 18 '21 at 9:32
• I found it and its derivation in this paper. It's based on a likelihood ratio test (section 5). Jun 18 '21 at 9:51
• Ah, thanks! That's interesting. Jun 18 '21 at 9:55