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Show that two random variables \({\bf{\mu }}\,\,{\bf{and}}\,\,{\bf{\tau }}\)cannot have the joint normal-gamma distribution such that

\({\bf{E}}\left( {\bf{\mu }} \right){\bf{ = 0}}\,\,{\bf{,Var}}\left( {\bf{\mu }} \right){\bf{ = 1,E}}\left( {\bf{\tau }} \right){\bf{ = }}\frac{{\bf{1}}}{{\bf{2}}}\,\,{\bf{and}}\,\,{\bf{Var}}\left( {\bf{\tau }} \right){\bf{ = }}\frac{{\bf{1}}}{{\bf{4}}}\)

Short Answer

Expert verified

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

Step by step solution

01

Given information

It is given that two variables \(\mu \,\,and\,\,\tau \)have the joint normal-gamma distribution such that \(E\left( \mu \right) = 0\,\,,Var\left( \mu \right) = 1,E\left( \tau \right) = \frac{1}{2}\,\,{\rm{and}}\,\,Var\left( \tau \right) = \frac{1}{4}\)

02

Define Normal-Gamma distribution

Let \(\mu \,\,and\,\,\tau \) be random variables. Suppose that the conditional distribution of \(\mu \,\,given\,\,\tau \) 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 \({\alpha _0}\,\,and\,\,{\beta _0}\). Then we say that the joint distribution of\(\mu \,\,and\,\,\tau \) 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,

\(E\left( \tau \right) = \frac{1}{2}\,\,{\rm{and}}\,\,Var\left( \tau \right) = \frac{1}{4}\)

So equating equations,

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

Solving (1) and (2)

\(\begin{align}{\alpha _0} &= 1\\{\beta _0} &= 2\end{align}\)

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

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Most popular questions from this chapter

When the motion of a microscopic particle in a liquid or a gas is observed, it is seen that the motion is irregular because the particle frequently collides with other particles. The probability model for this motion, which is called Brownian motion,is as follows: A coordinate system is chosen in the liquid or gas. Suppose that the particle is at the origin of this coordinate system at timet=0, and let(X, Y, Z)denote the particle's coordinates at any timet >0. The random variablesX,Y, andZare i.i.d. Each has a normal distribution with mean 0 and variance\({\sigma ^2}t\). Find the probability that at timet=2, the particle will lie within a sphere whose centre is at the origin and whose radius is 4ฯƒ.

Consider again the conditions of Exercise 19, and let\({{\bf{\hat \beta }}_{\bf{n}}}\)n denote the M.L.E. of ฮฒ.

a. Use the delta method to determine the asymptotic distribution of\(\frac{{\bf{1}}}{{{{{\bf{\hat \beta }}}_{\bf{n}}}}}\).

b. Show that\(\frac{{\bf{1}}}{{{{{\bf{\hat \beta }}}_{\bf{n}}}}}{\bf{ = }}{{\bf{\bar X}}_{\bf{n}}}\), and use the central limit theorem to determine the asymptotic distribution of\(\frac{{\bf{1}}}{{{{{\bf{\hat \beta }}}_{\bf{n}}}}}\).

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.

Suppose that\({X_1}...{X_n}\)form a random sample from the normal distribution with mean 0 and unknown standard deviation\(\sigma > 0\). Find the lower bound specified by the information inequality for the variance of any unbiased estimator of\(\log \sigma \).

Complete the proof of Theorem 8.5.3 by dealing with the case in which r(v, x) is strictly decreasing in v for each x.

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