Consider the normal linear model $Y=X \beta+\varepsilon$ where $X$ is a known $n \times p$ design matrix with $n-2>p \geqslant 1, \beta \in \mathbb{R}^{p}$ is an unknown vector of parameters, and $\varepsilon \sim N_{n}\left(0, \sigma^{2} I\right)$ is a vector of normal errors with each component having variance $\sigma^{2}>0$. Suppose $X$ has full column rank.

(i) Write down the maximum likelihood estimators, $\hat{\beta}$ and $\hat{\sigma}^{2}$, for $\beta$ and $\sigma^{2}$ respectively. [You need not derive these.]

(ii) Show that $\hat{\beta}$ is independent of $\hat{\sigma}^{2}$.

(iii) Find the distributions of $\hat{\beta}$ and $n \hat{\sigma}^{2} / \sigma^{2}$.

(iv) Consider the following test statistic for testing the null hypothesis $H_{0}: \beta=0$ against the alternative $\beta \neq 0$ :

$$T:=\frac{\|\hat{\beta}\|^{2}}{n \hat{\sigma}^{2}} .$$

Let $\lambda_{1} \geqslant \lambda_{2} \geqslant \cdots \geqslant \lambda_{p}>0$ be the eigenvalues of $X^{T} X$. Show that under $H_{0}, T$ has the same distribution as

$$\frac{\sum_{j=1}^{p} \lambda_{j}^{-1} W_{j}}{Z}$$

where $Z \sim \chi_{n-p}^{2}$ and $W_{1}, \ldots, W_{p}$ are independent $\chi_{1}^{2}$ random variables, independent of $Z$.

[Hint: You may use the fact that $X=U D V^{T}$ where $U \in \mathbb{R}^{n \times p}$ has orthonormal columns, $V \in \mathbb{R}^{p \times p}$ is an orthogonal matrix and $D \in \mathbb{R}^{p \times p}$ is a diagonal matrix with $\left.D_{i i}=\sqrt{\lambda_{i}} .\right]$

(v) Find $\mathbb{E} T$ when $\beta \neq 0$. [Hint: If $R \sim \chi_{\nu}^{2}$ with $\nu>2$, then $\mathbb{E}(1 / R)=1 /(\nu-2)$.]