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

Short Answer

Expert verified

The limit formula of the equation 8.4.6 is \(\mathop {\lim }\limits_{m \to \infty } \frac{{\left| \!{\overline {\, {\left( {m + \frac{1}{2}} \right)} \,}} \right. }}{{\left| \!{\overline {\, {m{{\left( m \right)}^{\frac{1}{2}}}} \,}} \right. }} = 1\)

Step by step solution

01

Given information

The pdf of the random variable X from t-distribution with n degrees of freedom is

\(f\left( x \right) = \frac{{\left| \!{\overline {\, {\left[ {\left( {\frac{{n + 1}}{2}} \right)} \right]} \,}} \right. }}{{{{\left( {n\pi } \right)}^{\frac{1}{2}}}\left| \!{\overline {\, {\left( {\frac{n}{2}} \right)} \,}} \right. }}{\left( {1 + \frac{{{x^2}}}{n}} \right)^{ - \left( {\frac{{n + 1}}{2}} \right)}}\)

It is needed to prove that

\(\mathop {\lim }\limits_{m \to \infty } \frac{{\left| \!{\overline {\, {\left( {m + \frac{1}{2}} \right)} \,}} \right. }}{{\left| \!{\overline {\, {m{{\left( m \right)}^{\frac{1}{2}}}} \,}} \right. }} = 1\)

02

Proof of \(\mathop {\lim }\limits_{m \to \infty } \frac{{\left| \!{\overline {\, {\left( {m + \frac{1}{2}} \right)} \,}} \right. }}{{\left| \!{\overline {\, {m{{\left( m \right)}^{\frac{1}{2}}}} \,}} \right. }} = 1\) 

From the limit formula of the equation 8.4.2 it can be shown that

\(\begin{align}\mathop {\lim }\limits_{m \to \infty } \frac{{\Gamma \left( {m + \frac{1}{2}} \right)}}{{\Gamma \left( m \right){m^{\frac{1}{2}}}}} &= \mathop {\lim }\limits_{m \to \infty } \frac{{{{\left( {2\pi } \right)}^{\frac{1}{2}}}{{\left( {m + \frac{1}{2}} \right)}^{\left( {m + \frac{1}{2}} \right) - \frac{1}{2}}}{e^{ - \left( {m + \frac{1}{2}} \right)}}}}{{{{\left( {2\pi } \right)}^{\frac{1}{2}}}{m^{\frac{{m - 1}}{2}}}{e^{ - m}}{m^{\frac{1}{2}}}}}\\ &= \mathop {\lim }\limits_{m \to \infty } \frac{{{{\left( {m + \frac{1}{2}} \right)}^m}}}{{{m^m}}}{e^{ - \frac{1}{2}}}\\ &= \mathop {\lim }\limits_{m \to \infty } {\left( {1 + \frac{{\frac{1}{2}}}{m}} \right)^m}{e^{ - \frac{1}{2}}}\\ &= {e^{\frac{1}{2}}}{e^{ - \frac{1}{2}}}\\ &= 1\end{align}\)

Hence the proof.

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Most popular questions from this chapter

Suppose thatXhas the\({\chi ^{\bf{2}}}\)distribution with 200 degrees of freedom. Explain why the central limit theorem can be used to determine the approximate value of Pr(160<X<240)and find this approximate value.

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

Question: Suppose a random variable X has the Poisson distribution with an unknown mean \({\bf{\lambda }}\) (\({\bf{\lambda }}\)>0). Find a statistic \({\bf{\delta }}\left( {\bf{X}} \right)\) that will be an unbiased estimator of \({{\bf{e}}^{\bf{\lambda }}}\).Hint: If \({\bf{E}}\left( {{\bf{\delta }}\left( {\bf{X}} \right)} \right){\bf{ = }}{{\bf{e}}^{\bf{\lambda }}}\) , then \(\sum\limits_{{\bf{x = 0}}}^\infty {\frac{{{\bf{\delta }}\left( {\bf{x}} \right){{\bf{e}}^{{\bf{ - \lambda }}}}{{\bf{\lambda }}^{\bf{x}}}}}{{{\bf{x!}}}}} = {{\bf{e}}^{\bf{\lambda }}}\)

Multiply both sides of this equation by \({{\bf{e}}^{\bf{\lambda }}}\)expanding the right side in a power series in \({\bf{\lambda }}\), and then equate the coefficients of \({{\bf{\lambda }}^{\bf{x}}}\) on both sides of the equation for x = 0, 1, 2, . . ..

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