Chapter 3: Problem 21
Show that the graph of the \(\beta\) pdf is symmetric about the vertical line through \(x=\frac{1}{2}\) if \(\alpha=\beta\).
/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none}
Learning Materials
Features
Discover
Chapter 3: Problem 21
Show that the graph of the \(\beta\) pdf is symmetric about the vertical line through \(x=\frac{1}{2}\) if \(\alpha=\beta\).
All the tools & learning materials you need for study success - in one app.
Get started for free
Let \(X\) be \(N(5,10)\). Find \(P\left[0.04<(X-5)^{2}<38.4\right]\).
Determine the constant \(c\) so that \(f(x)=c x(3-x)^{4}, 0
Let \(X\) have the conditional Weibull pdf
$$f(x \mid \theta)=\theta \tau x^{\tau-1} e^{-\theta x^{\top}}, \quad
0
Readers may have encountered the multiple regression model in a previous course in statistics. We can briefly write it as follows. Suppose we have a vector of \(n\) observations \(\mathbf{Y}\) which has the distribution \(N_{n}\left(\mathbf{X} \boldsymbol{\beta}, \sigma^{2} \mathbf{I}\right)\), where \(\mathbf{X}\) is an \(n \times p\) matrix of known values, which has full column rank \(p\), and \(\beta\) is a \(p \times 1\) vector of unknown parameters. The least squares estimator of \(\boldsymbol{\beta}\) is $$ \widehat{\boldsymbol{\beta}}=\left(\mathbf{X}^{\prime} \mathbf{X}\right)^{-1} \mathbf{X}^{\prime} \mathbf{Y} $$ (a) Determine the distribution of \(\widehat{\boldsymbol{\beta}}\). (b) Let \(\hat{\mathbf{Y}}=\mathbf{X} \hat{\boldsymbol{\beta}}\). Determine the distribution of \(\widehat{\mathbf{Y}}\) (c) Let \(\widehat{\mathbf{e}}=\mathbf{Y}-\hat{\mathbf{Y}}\). Determine the distribution of \(\widehat{\mathbf{e}}\). (d) By writing the random vector \(\left(\widehat{\mathbf{Y}}^{\prime}, \widehat{\mathbf{e}}^{\prime}\right)^{\prime}\) as a linear function of \(\mathbf{Y}\), show that the random vectors \(\hat{\mathbf{Y}}\) and \(\widehat{\mathbf{e}}\) are independent. (e) Show that \(\widehat{\beta}\) solves the least squares problem; that is, $$\|\mathbf{Y}-\mathbf{X} \widehat{\boldsymbol{\beta}}\|^{2}=\min _{\mathbf{b} \in R^{p}}\|\mathbf{Y}-\mathbf{X} \mathbf{b}\|^{2}$$
Let \(X_{1}, X_{2}, X_{3}\) be iid random variables each having a standard normal distribution. Let the random variables \(Y_{1}, Y_{2}, Y_{3}\) be defined by $$X_{1}=Y_{1} \cos Y_{2} \sin Y_{3}, \quad X_{2}=Y_{1} \sin Y_{2} \sin Y_{3}, \quad X_{3}=Y_{1} \cos Y_{3}$$ where \(0 \leq Y_{1}<\infty, 0 \leq Y_{2}<2 \pi, 0 \leq Y_{3} \leq \pi\). Show that \(Y_{1}, Y_{2}, Y_{3}\) are mutually independent.
What do you think about this solution?
We value your feedback to improve our textbook solutions.