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Question:Suppose that a random variable X has a normal distribution for which the mean μ is unknown (−∞ <μ< ∞) and the variance σ2 is known. Let\({\bf{f}}\left( {{\bf{x}}\left| {\bf{\mu }} \right.} \right)\)denote the p.d.f. of X, and let\({\bf{f'}}\left( {{\bf{x}}\left| {\bf{\mu }} \right.} \right)\)and\({\bf{f''}}\left( {{\bf{x}}\left| {\bf{\mu }} \right.} \right)\)denote the first and second partial derivatives with respect to μ. Show that

\(\int_{{\bf{ - }}\infty }^\infty {{\bf{f'}}\left( {{\bf{x}}\left| {\bf{\mu }} \right.} \right)} {\bf{dx = 0}}\,\,{\bf{and}}\,\,\int_{{\bf{ - }}\infty }^\infty {{\bf{f''}}\left( {{\bf{x}}\left| {\bf{\mu }} \right.} \right)} {\bf{dx = 0}}{\bf{.}}\).

Short Answer

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Proved.

Step by step solution

01

Given information

X has a normal distribution with the mean of\(\mu \)and the variance of\({\sigma ^2}\).

\(f\left( {x\left| \mu \right.} \right)\)denote the p.d.f. of X

\(f'\left( {x\left| \mu \right.} \right)\) and \(f''\left( {x\left| \mu \right.} \right)\) denote the first and second-order partial derivatives with respect to \(\mu \)

02

Proving part

\(f\left( {x\left| \mu \right.} \right) = \frac{1}{{\sqrt {2\pi } \sigma }}\exp \left\{ { - \frac{1}{{2{\sigma ^2}}}{{\left( {x - \mu } \right)}^2}} \right\}\)

So,

The first-order derivative is,

\(\begin{array}{c}\frac{\partial }{{\partial \mu }}f\left( {x\left| \mu \right.} \right) = \frac{\partial }{{\partial \mu }}\left( {\frac{1}{{\sqrt {2\pi } \sigma }}\exp \left\{ { - \frac{1}{{2{\sigma ^2}}}{{\left( {x - \mu } \right)}^2}} \right\}} \right)\\ = \frac{1}{{\sqrt {2\pi } \sigma }}\frac{{\left( {x - \mu } \right)}}{{{\sigma ^2}}}\exp \left\{ { - \frac{1}{{2{\sigma ^2}}}{{\left( {x - \mu } \right)}^2}} \right\}\\ = \frac{{\left( {x - \mu } \right)}}{{{\sigma ^2}}}f\left( {x\left| \mu \right.} \right)\end{array}\)

The second-order derivative is,

\(\begin{array}{c}\frac{\partial }{{\partial \mu }}f'\left( {x\left| \mu \right.} \right) = \frac{\partial }{{\partial \mu }}\left( {\frac{{\left( {x - \mu } \right)}}{{{\sigma ^2}}}f\left( {x\left| \mu \right.} \right)} \right)\\ = \left[ {\frac{{{{\left( {x - \mu } \right)}^2}}}{{{\sigma ^4}}} - \frac{1}{{{\sigma ^2}}}} \right]f\left( {x\left| \mu \right.} \right)\end{array}\)

Therefore,

\(\begin{array}{c}\int_{ - \infty }^\infty {f'\left( {x\left| \mu \right.} \right)} dx = \frac{1}{{{\sigma ^2}}}\int_{ - \infty }^\infty {\left( {x - \mu } \right)} f\left( {x\left| \mu \right.} \right)d\mu \\ = \frac{1}{{{\sigma ^2}}}E\left( {X - \mu } \right)\\ = \frac{1}{{{\sigma ^2}}}\left( {\mu - \mu } \right)\\ = 0\end{array}\)

Hence, proved.

Again,

\(\begin{array}{c}\int_{ - \infty }^\infty {f''\left( {x\left| \mu \right.} \right)} dx = \frac{1}{{{\sigma ^4}}}E\left[ {{{\left( {X - \mu } \right)}^2}} \right] - \frac{1}{{{\sigma ^2}}}\\ = \frac{{{\sigma ^4}}}{{{\sigma ^2}}} - \frac{1}{{{\sigma ^2}}}\\ = \frac{1}{{{\sigma ^2}}} - \frac{1}{{{\sigma ^2}}}\\ = 0\end{array}\)

Hence, proved.

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Most popular questions from this chapter

Prove that the distribution of\({\hat \sigma _0}^2\)in Examples 8.2.1and 8.2.2 is the gamma distribution with parameters\(\frac{n}{2}\)and\(\frac{n}{{2{\sigma ^2}}}\).

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 possibleθvalues consistent with the observed data.
  3. Is the 50% confidence interval larger or smaller than the set of possibleθvalues?
  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: 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.

In the situation of Exercise 9, suppose that a prior distribution is used forθwith p.d.f.ξ(θ)=0.1 exp(−0.1θ)forθ >0. (This is the exponential distribution with parameter 0.1.)

  1. Prove that the posterior p.d.f. ofθgiven the data observed in Exercise 9 is

\({\bf{\xi }}\left( {{\bf{\theta }}\left| {\bf{x}} \right.} \right){\bf{ = }}\left\{ \begin{align}{l}{\bf{4}}{\bf{.122exp}}\left( {{\bf{ - 0}}{\bf{.1\theta }}} \right)\;{\bf{if}}\;{\bf{4}}{\bf{.8 < \theta < 5}}{\bf{.2}}\\{\bf{0}}\;{\bf{otherwise}}\end{align} \right.\)

  1. Calculate the posterior probability \(\left| {{\bf{\theta - }}{{{\bf{\bar X}}}_{\bf{2}}}} \right|{\bf{ < 0}}{\bf{.1}}\), which \({{\bf{\bar X}}_{\bf{2}}}\)is the observed average of the datavalues.
  2. Calculate the posterior probability thatθis in the confidence interval found in part (a) of Exercise 9.
  3. Can you explain why the answer to part (b) is so close to the answer to part (e) of Exercise 9? Hint:Compare the posterior p.d.f. in part (a) to the function in Eq. (8.5.15).

Suppose a random variable has a normal distribution with a mean of 0 and an unknown standard deviation σ> 0. Find the Fisher information I (σ) in X.

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