Warning: foreach() argument must be of type array|object, bool given in /var/www/html/web/app/themes/studypress-core-theme/template-parts/header/mobile-offcanvas.php on line 20

Suppose that\({{\bf{X}}_{\bf{1}}}{\bf{,}}{{\bf{X}}_{\bf{2}}}{\bf{,}}...{\bf{,}}{{\bf{X}}_{\bf{n}}}\)form a random sample from the uniform distribution on the interval\(\left( {{\bf{0,1}}} \right)\), and let\({\bf{W}}\)denote the range of the sample, as defined in Example 3.9.7. Also, let\({{\bf{g}}_{\bf{n}}}\left( {\bf{x}} \right)\)denote the p.d.f of the random

variable\({\bf{2n}}\left( {{\bf{1 - W}}} \right)\), and let\({\bf{g}}\left( {\bf{x}} \right)\)denote the p.d.f of the\({\chi ^{\bf{2}}}\)distribution with four degrees of freedom. Show that

\(\mathop {{\bf{lim}}}\limits_{{\bf{n}} \to \infty } {{\bf{g}}_{\bf{n}}}\left( {\bf{x}} \right){\bf{ = g}}\left( {\bf{x}} \right)\) for\({\bf{x > 0}}\).

Short Answer

Expert verified

\(\mathop {\lim }\limits_{n \to \infty } {g_n}\left( x \right) = g\left( x \right)\) for \(x > 0\)

Step by step solution

01

Given information 

\({X_1},{X_2},...,{X_n}\) be a uniform random sample.

\(W\)denote the range of the sample and \({g_n}\left( x \right)\)denote the p.d.f of\(2n\left( {1 - W} \right)\).

\(g\left( x \right)\) denote the p.d.f of the \({\chi ^2}\)distribution with degrees of freedom 4.

02

Determine the p.d.f of \({\bf{W}}\) 

The p.d.f of \(W\)is

\({h_1}\left( w \right) = n\left( {n - 1} \right){w^{n - 2}}\left( {1 - w} \right)\)for\(0 < w < 1\)

Let,\(X = 2n\left( {1 - W} \right)\)

\(\begin{align} \Rightarrow W &= \frac{{1 - X}}{{2n}}\\ \Rightarrow \frac{{dW}}{{dx}} &= - \frac{1}{{2n}}\end{align}\)

03

Determine the p.d.f of \({{\bf{g}}_{\bf{n}}}\left( {\bf{x}} \right)\) 

The p.d.f of \({g_n}\left( x \right)\)is

\(\begin{align}{g_n}\left( x \right) &= {h_1}\left( {1 - \frac{x}{{2n}}} \right)\left| {\frac{{dW}}{{dx}}} \right|\\ &= n\left( {n - 1} \right){\left( {1 - \frac{x}{{2n}}} \right)^{n - 2}}\left( {\frac{x}{{2n}}} \right)\left( {\frac{1}{{2n}}} \right)\\ &= \frac{1}{4}\left( {\frac{{n - 1}}{n}} \right)x{\left( {1 - \frac{x}{{2n}}} \right)^{ - 2}}{\left( {1 - \frac{x}{{2n}}} \right)^n}\end{align}\) for \(0 < x < 2n\)

As\(n \to \infty \),

\(\begin{align}\frac{{n - 1}}{n} \to 1\\{\left( {1 - \frac{x}{{2n}}} \right)^{ - 2}} \to 1\end{align}\)

For any real number\(t\),

\({\left( {\frac{{1 + t}}{n}} \right)^n} \to \exp \left( t \right)\)

So,\({\left( {1 - \frac{x}{{2n}}} \right)^n} \to \exp \left( { - \frac{x}{2}} \right)\)

Hence, for\(x > 0\),

\({g_n}\left( x \right) \to \frac{1}{4}x\exp \left( { - \frac{x}{2}} \right)\)

Hence,\(\mathop {\lim }\limits_{n \to \infty } {g_n}\left( x \right) = g\left( x \right)\)for\(x > 0\).

Hence, proved.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with Vaia!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

For the conditions of Exercise 2, how large a random sample must be taken in order that\({\bf{P}}\left( {{\bf{|}}{{{\bf{\bar X}}}_{\bf{n}}}{\bf{ - \theta |}} \le {\bf{0}}{\bf{.1}}} \right) \ge {\bf{0}}{\bf{.95}}\)for every possible value ofθ?

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.

Question: Consider the situation described in Exercise 7 of Sec. 8.5. Use a prior distribution from the normal-gamma family with values \({{\bf{\mu }}_{\bf{0}}}{\bf{ = 150,}}{{\bf{\lambda }}_{\bf{0}}}{\bf{ = 0}}{\bf{.5,}}{{\bf{\alpha }}_{\bf{0}}}{\bf{ = 1,}}{{\bf{\beta }}_{\bf{0}}}{\bf{ = 4}}\)

a. Find the posterior distribution of \({\bf{\mu }}\,\,{\bf{and}}\,\,{\bf{\tau = }}\frac{{\bf{1}}}{{{{\bf{\sigma }}^{\bf{2}}}}}\)

b. Find an interval (a, b) such that the posterior probability is 0.90 that a <\({\bf{\mu }}\)<b.

Suppose thatXhas thetdistribution withmdegrees of freedom(m >2). Show that Var(X)=m/(m−2).

Hint:To evaluate\({\bf{E}}\left( {{{\bf{X}}^{\bf{2}}}} \right)\), restrict the integral to the positive half of the real line and change the variable fromxto

\({\bf{y = }}\frac{{\frac{{{{\bf{x}}^{\bf{2}}}}}{{\bf{m}}}}}{{{\bf{1 + }}\frac{{{{\bf{x}}^{\bf{2}}}}}{{\bf{m}}}}}\)

Compare the integral with the p.d.f. of a beta distribution. Alternatively, use Exercise 21 in Sec. 5.7.

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.
See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.

Sign-up for free