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

Show that two random variables \({\bf{\mu }}\,\,{\bf{and}}\,\,{\bf{\tau }}\)cannot have the joint normal-gamma distribution such that \({\bf{E}}\left( {\bf{\mu }} \right){\bf{ = 0}}\,\,{\bf{,E}}\left( {\bf{\tau }} \right){\bf{ = 1}}\,\,{\bf{and}}\,\,{\bf{Var}}\left( {\bf{\tau }} \right){\bf{ = 4}}\)

Short Answer

Expert verified

The conditions imply that \({\alpha _0} = \frac{1}{4}\)and \(E\left( \mu \right)\) exist only for \({\alpha _0} > \frac{1}{2}\)\({\alpha _0}\,\,{\rm{and}}\,\,{\beta _0}\)

Step by step solution

01

Given information

It is given that two variables \(\mu \,\,and\,\,\tau \)have the joint normal-gamma distribution such that \(E\left( \mu \right) = 0\,\,,E\left( \tau \right) = 1\,\,and\,\,Var\left( \tau \right) = 4\)

02

Define Normal-Gamma distribution

Let \(\mu \,\,{\rm{and}}\,\,\tau \) be random variables. Suppose that the conditional distribution of \(\mu \,\,{\rm{given}}\,\,\tau \) is the normal distribution with mean \({\mu _0}\) and precision \({\lambda _0}\tau \) .

Suppose also that the marginal distribution of \(\,\tau \) is the gamma distribution with parameters .

Then we say that the joint distribution of\(\mu \,\,{\rm{and}}\,\,\tau \) is the normal-gamma distribution with hyperparameters \({\mu _0},{\lambda _0},{\alpha _0},{\beta _0}\).

03

Solve for \({{\bf{\alpha }}_{\bf{0}}}{\bf{,}}{{\bf{\beta }}_{\bf{0}}}\)

In the definition, it is given that the marginal distribution of \(\,\tau \) is the gamma distribution with parameters \({\alpha _0}\,\,and\,\,{\beta _0}\).

Therefore, by properties of the gamma distribution,

\(E\left( {\,\tau } \right) = \frac{{{\alpha _0}\,}}{{{\beta _0}}},Var\left( {\,\tau } \right) = \frac{{{\alpha _0}\,}}{{{\beta _0}^2}}\)

But it is given that,

\(E\left( \tau \right) = 1\,\,and\,\,Var\left( \tau \right) = 4\)

So equating equations,

\(\begin{align}\frac{{{\alpha _0}\,}}{{{\beta _0}}} &= 1 \ldots \left( 1 \right)\\\frac{{{\alpha _0}\,}}{{{\beta _0}^2}} &= 4 \ldots \left( 2 \right)\end{align}\)

Solving (1) and (2)

\({\alpha _0} = {\beta _0} = \frac{1}{4}\)

The conditions imply that \({\alpha _0} = \frac{1}{4}\)and \(E\left( \mu \right)\) which is equal to 0 exists only for \({\alpha _0} > \frac{1}{2}\)

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

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

In the situation of Example 8.5.11, suppose that we observe\({{\bf{X}}_{\bf{1}}}{\bf{ = 4}}{\bf{.7}}\;{\bf{and}}\;{{\bf{X}}_{\bf{2}}}{\bf{ = 5}}{\bf{.3}}\).

  1. Find the 50% confidence interval described in Example 8.5.11.
  2. Find the interval of possibleฮธvalues consistent with the observed data.
  3. Is the 50% confidence interval larger or smaller than the set of possibleฮธvalues?
  4. Calculate the value of the random variable\({\bf{Z = }}{{\bf{Y}}_{\bf{2}}}{\bf{ - }}{{\bf{Y}}_{\bf{1}}}\)as described in Example 8.5.11.
  5. Use Eq. (8.5.15) to compute the conditional probability that\(\left| {{{{\bf{\bar X}}}_{\bf{2}}}{\bf{ - \theta }}} \right|{\bf{ < 0}}{\bf{.1}}\)givenZ isequal to the value calculated in part (d).

Question: Prove the limit formula Eq. (8.4.6).

Question:Suppose that a random variable X has the geometric distribution with an unknown parameter p. (See Sec. 5.5.).Find a statistic \({\bf{\delta }}\left( {\bf{X}} \right)\)that will be an unbiased estimator of\(\frac{{\bf{1}}}{{\bf{p}}}\).

Question: Suppose that a random variable X has the Poisson distribution with unknown mean \({\bf{\theta }}\) >0. Find the Fisher information \({\bf{I}}\left( {\bf{\theta }} \right)\) in X.

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