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Suppose that a random sampleX1, . . . , Xnis to be taken from the uniform distribution on the interval (0, θ) and thatθis unknown. How large must a random sample be taken in order\({\bf{P}}\left( {{\bf{|max}}\left\{ {{{\bf{X}}_{\bf{1}}}{\bf{,}}...{\bf{,}}{{\bf{X}}_{\bf{n}}}} \right\}{\bf{ - \theta |}} \le {\bf{0}}{\bf{.10}}} \right) \ge {\bf{0}}{\bf{.95}}\) for all possibleθ?

Short Answer

Expert verified

The needed sample size is \(n \ge 29\)

Step by step solution

01

Given information

The samples X1,.., and Xn come from a uniform distribution

02

Finding the sample size

Let \(U = \max \left\{ {{X_1},...,{X_n}} \right\}\)

The CDF of U is

\(F\left( u \right) = \left\{ \begin{align}{l}0\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,for\,u \le 0\\{\left( {u/\theta } \right)^n}\,\,\,\,\,\,for\,0 < u < \theta \\1\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,for\,u \ge \theta \end{align} \right.\)

Since, \(U \le \theta \) with probability 1, the event \(|U - \theta | \le 0.10\) is the same as the probability of this \(1 - F\left( {0.9\theta } \right) = 1 - {0.9^n}\) .

For this to be at least 0.95, we need

\(\begin{array}{0.9^n} \le 0.05\\n \ge \log \left( {0.05} \right)/\log \left( {0.9} \right)\\n \ge 28.43\end{array}\)

So, \(n \ge 29\) is needed.

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Most popular questions from this chapter

Question:Suppose that a specific population of individuals is composed of k different strata (k ≥ 2), and that for i = 1,...,k, the proportion of individuals in the total population who belong to stratum i is pi, where pi > 0 and\(\sum\nolimits_{{\bf{i = 1}}}^{\bf{k}} {{{\bf{p}}_{\bf{i}}}{\bf{ = 1}}} \). We are interested in estimating the mean value μ of a particular characteristic among the total population. Among the individuals in stratum i, this characteristic has mean\({{\bf{\mu }}_{\bf{i}}}\)and variance\({\bf{\sigma }}_{\bf{i}}^{\bf{2}}\), where the value of\({{\bf{\mu }}_{\bf{i}}}\)is unknown and the value of\({\bf{\sigma }}_{\bf{i}}^{\bf{2}}\)is known. Suppose that a stratified sample is taken from the population as follows: From each stratum i, a random sample of ni individuals is taken, and the characteristic is measured for each individual. The samples from the k strata are taken independently of each other. Let\({{\bf{\bar X}}_{\bf{i}}}\)denote the average of the\({{\bf{n}}_{\bf{i}}}\)measurements in the sample from stratum i.

a. Show that\({\bf{\mu = }}\sum\nolimits_{{\bf{i = 1}}}^{\bf{k}} {{{\bf{p}}_{\bf{i}}}{{\bf{\mu }}_{\bf{i}}}} \), and show also that\({\bf{\hat \mu = }}\sum\nolimits_{{\bf{i = 1}}}^{\bf{k}} {{{\bf{p}}_{\bf{i}}}{{{\bf{\bar X}}}_{\bf{i}}}} \)is an unbiased estimator of μ.

b. Let\({\bf{n = }}\sum\nolimits_{{\bf{i = 1}}}^{\bf{k}} {{{\bf{n}}_{\bf{i}}}} \)denote the total number of observations in the k samples. For a fixed value of n, find the values for which the variance \({\bf{\hat \mu }}\)will be a minimum.

Suppose that\({{\bf{X}}_{\bf{1}}}{\bf{,}}...{\bf{,}}{{\bf{X}}_{\bf{n}}}\)form a random sample from the Poisson distribution with unknown mean θ, and let

\({\bf{Y = }}\sum\nolimits_{{\bf{i = 1}}}^{\bf{n}} {{{\bf{X}}_{\bf{i}}}} \).

a. Determine the value of a constant c such that the estimator\({{\bf{e}}^{{\bf{ - cY}}}}\)is an unbiased estimator of\({{\bf{e}}^{{\bf{ - \theta }}}}\).

b. Use the information inequality to obtain a lower bound for the variance of the unbiased estimator found in part (a).

Assume thatX1, . . . , Xnfrom a random sample from the normal distribution with meanμand variance \({\sigma ^2}\). Show that \({\hat \sigma ^2}\)has the gamma distribution with parameters \(\frac{{\left( {n - 1} \right)}}{2}\)and\(\frac{n}{{\left( {2{\sigma ^2}} \right)}}\).

Suppose that a random sample of eight observations is taken from the normal distribution with unknown meanμand unknown variance\({{\bf{\sigma }}^{\bf{2}}}\), and that the observed values are 3.1, 3.5, 2.6, 3.4, 3.8, 3.0, 2.9, and 2.2. Find the shortest confidence interval forμwith each of the following three confidence coefficients:

  1. 0.90
  2. 0.95
  3. 0.99.

At the end of Example 8.5.11, compute the probability that \(\left| {{{\bar X}_2} - \theta } \right| < 0.3\) given Z = 0.9. Why is it so large?

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