Chapter 11: Problem 2
In the proof of Theorem 11.1.1, we considered the case in which
\(p_{3}
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Chapter 11: Problem 2
In the proof of Theorem 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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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 \(\theta\).
Let \(X_{1}, X_{2}, \ldots, X_{10}\) be a random sample of size \(n=10\) from a gamma distribution with \(\alpha=3\) and \(\beta=1 / \theta\). Suppose we believe that \(\theta\) has a gamma distribution with \(\alpha=10\) and \(\beta=2\). (a) Find the posterior distribution of \(\theta\). (b) If the observed \(\bar{x}=18.2\), what is the Bayes point estimate associated with square-error loss function? (c) What is the Bayes point estimate using the mode of the posterior distribution? (d) Comment on an HDR interval estimate for \(\theta\). Would it be easier to find one having equal tail probabilities? Hint: Can the posterior distribution be related to a chi-square distribution?
Let \(X_{1}, X_{2}\) be a random sample from a Cauchy distribution with pdf
$$
f\left(x ; \theta_{1}, \theta_{2}\right)=\left(\frac{1}{\pi}\right)
\frac{\theta_{2}}{\theta_{2}^{2}+\left(x-\theta_{1}\right)^{2}},
\quad-\infty
In Example 11.2.2, let \(n=30, \alpha=10\), and \(\beta=5\), so that \(\delta(y)=(10+y) / 45\) is the Bayes estimate of \(\theta\). (a) If \(Y\) has a binomial distribution \(b(30, \theta)\), compute the risk \(E\left\\{[\theta-\delta(Y)]^{2}\right\\}\). (b) Find values of \(\theta\) for which the risk of part (a) is less than \(\theta(1-\theta) / 30\), the risk associated with the maximum likelihood estimator \(Y / n\) of \(\theta\).
Let \(Y\) have a binomial distribution in which \(n=20\) and \(p=\theta\). The prior probabilities on \(\theta\) are \(P(\theta=0.3)=2 / 3\) and \(P(\theta=0.5)=1 / 3 .\) If \(y=9\), what are the posterior probabilities for \(\theta=0.3\) and \(\theta=0.5\) ?
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