# 23.5. Exercises#

1. A random vector $$\mathbf{Y} = [Y_1 ~~ Y_2 ~~ \cdots ~~ Y_n]^T$$ has mean vector $$\boldsymbol{\mu}$$ and covariance matrix $$\sigma^2 \mathbf{I}_n$$ where $$\sigma > 0$$ is a number and $$\mathbf{I}_n$$ is the $$n \times n$$ identity matrix.

(a) Pick one option and explain: $$Y_1$$ and $$Y_2$$ are

$$~~~~~$$ (i) independent. $$~~~~~~~~$$ (ii) uncorrelated but might not be independent. $$~~~~~~~~$$ (iii) not uncorrelated.

(b) Pick one option and explain: $$Var(Y_1)$$ and $$Var(Y_2)$$ are

$$~~~~~$$ (i) equal. $$~~~~~~~~$$ (ii) possibly equal, but might not be. $$~~~~~~~~$$ (iii) not equal.

(c) For $$m \le n$$ let $$\mathbf{A}$$ be an $$m \times n$$ matrix of real numbers, and let $$\mathbf{b}$$ be an $$m \times 1$$ vector of real numbers. Let $$\mathbf{V} = \mathbf{AY} + \mathbf{b}$$. Find the mean vector $$\boldsymbol{\mu}_\mathbf{V}$$ and covariance matrix $$\boldsymbol{\Sigma}_\mathbf{V}$$ of $$\mathbf{V}$$.

(d) Let $$\mathbf{c}$$ be an $$m \times 1$$ vector of real numbers and let $$W = \mathbf{c}^T\mathbf{V}$$ for $$\mathbf{V}$$ defined in Part (c). In terms of $$\mathbf{c}$$, $$\boldsymbol{\mu}_\mathbf{V}$$ and $$\boldsymbol{\Sigma}_\mathbf{V}$$, find $$E(W)$$ and $$Var(W)$$.

2. Let $$[U ~ V ~ W]^T$$ be multivariate normal with mean vector $$[0 ~ 0 ~ 0]^T$$ and covariance matrix $$\begin{bmatrix} 1 & \rho_1 & \rho_2 \\ \rho_1 & 1 & \rho_3 \\ \rho_2 & \rho_3 & 1 \end{bmatrix}$$

(a) What is the distribution of $$U$$?

(b) What is the distribution of $$U+2V$$?

(c) What is the joint distribution of $$U$$ and $$U+2V$$?

(d) Under what condition on the parameters is $$U$$ independent of $$U+2V$$?

3. Let $$[X_1 ~~ X_2 ~~ X_3]^T$$ be multivariate normal with mean vector $$\boldsymbol{\mu}$$ and covariance matrix $$\boldsymbol{\Sigma}$$ given by

$\begin{split} \boldsymbol{\mu} ~ = ~ \begin{bmatrix} \mu \\ \mu \\ \mu \end{bmatrix} ~~~~~~~~~~~ \boldsymbol{\Sigma} ~ = ~ \begin{bmatrix} \sigma_1^2 & \sigma_{12} & \sigma_{13} \\ \sigma_{21} & \sigma_2^2 & \sigma_{23} \\ \sigma_{31} & \sigma_{32} & \sigma_3^2 \end{bmatrix} \end{split}$

Find $$P\big{(} (X_1 + X_2)/2 < X_3 + 1 \big{)}$$.

4. Let $$X$$ be standard normal. Construct a random variable $$Y$$ as follows:

• Toss a fair coin.

• If the coin lands heads, let $$Y = X$$.

• If the coin lands tails, let $$Y = -X$$.

(a) Find the cdf of $$Y$$ and hence identify the distribution of $$Y$$.

(b) Find $$E(XY)$$ by conditioning on the result of the toss.

(c) Are $$X$$ and $$Y$$ uncorrelated?

(d) Are $$X$$ and $$Y$$ independent?

(e) Is the joint distribution of $$X$$ and $$Y$$ bivariate normal?

5. Normal Sample Mean and Sample Variance, Part 1

Let $$X_1, X_2, \ldots, X_n$$ be i.i.d. with mean $$\mu$$ and variance $$\sigma^2$$. Let

$\bar{X} ~ = ~ \frac{1}{n} \sum_{i=1}^n X_i$

denote the sample mean and

$S^2 ~=~ \frac{1}{n-1} \sum_{i=1}^n (X_i - \bar{X})^2$

denote the sample variance as defined earlier in the course.

(a) For $$1 \le i \le n$$ let $$D_i = X_i - \bar{X}$$. Find $$Cov(D_i, \bar{X})$$.

(b) Now assume in addition that $$X_1, X_2, \ldots, X_n$$ are i.i.d. normal $$(\mu, \sigma^2)$$. What is the joint distribution of $$\bar{X}, D_1, D_2, \ldots, D_{n-1}$$? Explain why $$D_n$$ isn’t on the list.

(c) True or false (justify your answer): The sample mean and sample variance of an i.i.d. normal sample are independent of each other.

6. Normal Sample Mean and Sample Variance, Part 2

(a) Let $$R$$ have the chi-squared distribution with $$n$$ degrees of freedom. What is the mgf of $$R$$?

(b) For $$R$$ as in Part (a), suppose $$R = V + W$$ where $$V$$ and $$W$$ are independent and $$V$$ has the chi-squared distribution with $$m < n$$ degrees of freedom. Can you identify the distribution of $$W$$? Justify your answer.

(c) Let $$X_1, X_2, \ldots , X_n$$ be any sequence of random variables and let $$\bar{X} = \frac{1}{n}\sum_{i=1}^n X_i$$. Let $$\alpha$$ be any constant. Prove the sum of squares decomposition

$\sum_{i=1}^n (X_i - \alpha)^2 ~=~ \sum_{i=1}^n (X_i - \bar{X})^2 ~+~ n(\bar{X} - \alpha)^2.$

(d) Now let $$X_1, X_2, \ldots, X_n$$ be i.i.d. normal with mean $$\mu$$ and variance $$\sigma^2 > 0$$. Let $$S^2$$ be the “sample variance” defined by

$S^2 ~=~ \frac{1}{n-1} \sum_{i=1}^n (X_i - \bar{X})^2.$

Find a constant $$c$$ such that $$cS^2$$ has a chi-squared distribution. Provide the degrees of freedom.

[Use Parts (b) and (c) as well as the result of the previous exercise.]