Chapter 1: Problem 17
Let \(\psi(t)=\log M(t)\), where \(M(t)\) is the mgf of a distribution. Prove that \(\psi^{\prime}(0)=\mu\) and \(\psi^{\prime \prime}(0)=\sigma^{2} .\) The function \(\psi(t)\) is called the cumulant generating function.
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Chapter 1: Problem 17
Let \(\psi(t)=\log M(t)\), where \(M(t)\) is the mgf of a distribution. Prove that \(\psi^{\prime}(0)=\mu\) and \(\psi^{\prime \prime}(0)=\sigma^{2} .\) The function \(\psi(t)\) is called the cumulant generating function.
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Given the cdf
$$
F(x)=\left\\{\begin{array}{ll}
0 & x<-1 \\
\frac{x+2}{4} & -1 \leq x<1 \\
1 & 1 \leq x
\end{array}\right.
$$
sketch the graph of \(F(x)\) and then compute: (a) \(P\left(-\frac{1}{2}
Let \(X\) have the pmf $$ p(x)=\left(\frac{1}{2}\right)^{|x|}, \quad x=-1,-2,-3, \ldots $$ Find the pmf of \(Y=X^{4}\).
If the pdf of \(X\) is \(f(x)=2 x e^{-x^{2}}, 0
Let \(X\) have the pdf \(f(x)=3 x^{2}, 0
Let \(X\) be a positive random variable; i.e., \(P(X \leq 0)=0\). Argue that (a) \(E(1 / X) \geq 1 / E(X)\) (b) \(E[-\log X] \geq-\log [E(X)]\) (c) \(E[\log (1 / X)] \geq \log [1 / E(X)]\) (d) \(E\left[X^{3}\right] \geq[E(X)]^{3}\).
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