 Solution

The Gaussian likelihood has log-likelihood, apart from no omitted parameter-dependent terms,For fixed ${\sigma }^2$, maximizing it is ordinary least squares. Differentiating with respect to $\beta$ gives the normal equation $X^{T}X\hat{\beta }=X^{T}Y$. Full column rank makes the quadratic strictly convex, soAt this value, differentiating with respect to $v={\sigma }^2$ gives $-n/(2v)+\mathrm{RSS}/(2v^2)=0$, soFor positive RSS this is the global likelihood maximum in $v>0$. RSS is positive almost surely under the model since $p<n$; an exactly zero observed RSS instead gives a boundary supremum as $v\downarrow 0$.

As a linear image of a multivariate normal vector, the coefficient estimate satisfiesIndeed, $\hat{\beta }-\beta =(X^{T}X{)}^{-1}{X}^T\epsilon$, and direct matrix multiplication gives its covariance matrix.