Chapter 11: Problem 2
. In the proof of \(11.1 .1\), we considered the case in which
\(p_{3}
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Chapter 11: Problem 2
. In the proof of \(11.1 .1\), we considered the case in which
\(p_{3}
These are the key concepts you need to understand to accurately answer the question.
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. Let \(Y_{4}\) be the largest order statistic of a sanple of size \(n=4\) from a
distribution with uniform pdf \(f(x ; \theta)=1 / \theta, 0
1\. Suppose \(Y\) has a \(\Gamma(1,1)\) distribution while \(X\) given \(Y\) has the
conditional pdf
$$
f(x \mid y)=\left\\{\begin{array}{ll}
e^{-(x-y)} & 0
Let \(X_{1}, X_{2}, \ldots, X_{n}\) denote a random sample from a Poisson distribution with mean \(\theta, 0<\theta<\infty\). Let \(Y=\sum_{1}^{n} X_{i} .\) Use the loss function to be \(\mathcal{L}[\theta, \delta(y)]=\) \([\theta-\delta(y)]^{2} .\) Let \(\theta\) be an observed value of the random variable \(\Theta .\) If \(\Theta\) has the pdf \(h(\theta)=\theta^{\alpha-1} e^{-\theta / \beta} / \Gamma(\alpha) \beta^{\alpha}\), for \(0<\theta<\infty\), zero elsewhere, where \(\alpha>0, \beta>0\) are known numbers, find the Bayes' solution \(\delta(y)\) for a point estimate for \(\theta\).
The following amounts are bet on horses \(A, B, C, D, E\) to win. \begin{tabular}{cr} Horse & Amount \\ \hline\(A\) & \(\$ 600,000\) \\ \(B\) & \(\$ 200,000\) \\ \(C\) & \(\$ 100,000\) \\ \(D\) & \(\$ 75,000\) \\ \(E\) & \(\$ 25,000\) \\ \hline Total & \(\$ 1,000,000\) \end{tabular}Suppose the track wants to take \(20 \%\) off the top, namely \(\$ 200,000\). Determine the payoff for winning with a two dollar bet on each of the five horses. (In this exercise, we do not concern ourselves with "place" and "show.") Hint: Figure out what would be a fair payoff so that the track does not take any money, (that is, the track's take is zero), and then compute \(80 \%\) of those payoffs.
Consider the Bayes model \(X_{i} \mid \theta, i=1,2, \ldots n \sim\) iid with distribution \(b(1, \theta), 0<\theta<1\) $$ \Theta \sim h(\theta)=1 $$ (a) Obtain the posterior pdf. (b) Assume squared error loss and obtain the Bayes estimate of \(\theta\).
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