Past exam of the mathematics course of the University of Cambridge / 2012 / iii / Paper 37 / 1 / a

Use the normal linear model $Y=X\beta +\epsilon$, with fixed full-column-rank design matrix $X$, independent errors ${\epsilon }_i\sim N(0,{\sigma }^2)$, and ${\sigma }^2>0$. Its log-likelihood isFor fixed ${\sigma }^2$, differentiating with respect to $\beta$ gives $X^T(Y-X\hat{\beta })=0$. Since $X^{T}X$ is positive-definite, the unique least-squares estimate isLet $\mathrm{RSS}=\Vert Y-X\hat{\beta }{\Vert }^2$. The variance score equation is $-n/(2{\sigma }^2)+\mathrm{RSS}/(2{\sigma }^4)=0$, givingThis is the maximum-likelihood estimator, whereas the residual standard error reported by linear-regression software is $s=\sqrt{\mathrm{RSS}/(n-p)}$. The latter uses the unbiased residual estimate of Gaussian noise variance. These formulas assume $n>p$ and positive RSS; exact interpolation gives a variance-boundary degeneracy rather than an interior maximum.