Chapter 3: Problem 16
Let the mutually independent random variables \(X_{1}, X_{2}\), and \(X_{3}\) be \(N(0,1)\), \(N(2,4)\), and \(N(-1,1)\), respectively. Compute the probability that exactly two of these three variables are less than zero.
Chapter 3: Problem 16
Let the mutually independent random variables \(X_{1}, X_{2}\), and \(X_{3}\) be \(N(0,1)\), \(N(2,4)\), and \(N(-1,1)\), respectively. Compute the probability that exactly two of these three variables are less than zero.
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One way of estimating the number of fish in a lake is the following capturerecapture sampling scheme. Suppose there are \(N\) fish in the lake where \(N\) is unknown. A specified number of fish \(T\) are captured, tagged, and released back to the lake. Then at a specified time and for a specified positive integer \(r\), fish are captured until the \(r t h\) tagged fish is caught. The random variable of interest is \(Y\) the number of nontagged fish caught. (a) What is the distribution of \(Y ?\) Identify all parameters. (b) What is \(E(Y)\) and the \(\operatorname{Var}(Y)\) ? (c) The method of moment estimate of \(N\) is to set \(Y\) equal to the expression for \(E(Y)\) and solve this equation for \(N .\) Call the solution \(\hat{N}\). Determine \(\hat{N}\). (d) Determine the mean and variance of \(\hat{N}\).
Let \(Y_{1}, \ldots, Y_{k}\) have a Dirichlet distribution with parameters \(\alpha_{1}, \ldots, \alpha_{k}, \alpha_{k+1}\). (a) Show that \(Y_{1}\) has a beta distribution with parameters \(\alpha=\alpha_{1}\) and \(\beta=\alpha_{2}+\) \(\cdots+\alpha_{k+1}\) (b) Show that \(Y_{1}+\cdots+Y_{r}, r \leq k\), has a beta distribution with parameters \(\alpha=\alpha_{1}+\cdots+\alpha_{r}\) and \(\beta=\alpha_{r+1}+\cdots+\alpha_{k+1}\) (c) Show that \(Y_{1}+Y_{2}, Y_{3}+Y_{4}, Y_{5}, \ldots, Y_{k}, k \geq 5\), have a Dirichlet distribution with parameters \(\alpha_{1}+\alpha_{2}, \alpha_{3}+\alpha_{4}, \alpha_{5}, \ldots, \alpha_{k}, \alpha_{k+1}\) Hint: Recall the definition of \(Y_{i}\) in Example \(3.3 .6\) and use the fact that the sum of several independent gamma variables with \(\beta=1\) is a gamma variable.
For this exercise, the reader must have access to a statistical package that obtains the binomial distribution. Hints are given for \(\mathrm{R}\) code, but other packages can be used too. (a) Obtain the plot of the pmf for the \(b(15,0.2)\) distribution. Using \(\mathrm{R}\), the following commands return the plot: \(x<-0: 15 ;\) plot \(\left(\operatorname{dbinom}(x, 15, .2)^{-} x\right)\) (b) Repeat part (a) for the binomial distributions with \(n=15\) and with \(p=\) \(0.10,0.20, \ldots, 0.90 .\) Comment on the shapes of the pmf's as \(p\) increases. Use the following \(\mathrm{R}\) segment: \(\mathrm{x}<-0: 15 ; \quad\) par \((\mathrm{mfrow}=\mathrm{c}(3,3)) ; \mathrm{p}<-1: 9 / 10\) for \((j\) in \(p)\left\\{\right.\) plot \(\left(\right.\) dbinom \(\left.(x, 15, j)^{\sim} x\right) ;\) title(paste \(\left.\left.(" p=", j)\right)\right\\}\) (c) Let \(Y=\frac{X}{n}\), where \(X\) has a \(b(n, 0.05)\) distribution. Obtain the plots of the pmfs of \(Y\) for \(n=10,20,50,200 .\) Comment on the plots (what do the plots seem to be converging to as \(n\) gets large? ).
Let \(X\) denote time until failure of a device and let \(r(x)\) denote the hazard
function of \(X\).
(a) If \(r(x)=c x^{b}\); where \(c\) and \(b\) are positive constants, show that \(X\)
has a Weibull distribution; i.e.,
$$
f(x)=\left\\{\begin{array}{ll}
c x^{b} \exp \left\\{-\frac{c x^{b+1}}{b+1}\right\\} & 0
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