Past exam of the mathematics course of the University of Cambridge / 2019 / ii / Paper 3 / 5J (Statistical Modelling)

Writing the log-likelihood as $\ell (\beta )=\log L(\beta )$, the Fisher information matrix is the expected negative Hessian matrixUnder the usual regularity conditions it is equivalently the expected outer product of the score function.

For independent observations from an exponential dispersion family with common known dispersion parameter ${\sigma }^2$, the generalized linear model log-likelihood iswhere ${\mu }_i=b^{\prime }({\theta }_i)$ and the link function satisfies $g({\mu }_i)={\eta }_i=x_i^T\beta$.

For the canonical link function, ${\theta }_i={\eta }_i=x_i^T\beta$. Differentiating the log-likelihood gives the score functionSincewhere $V$ is the variance function, a second differentiation givesThis Hessian does not depend on the observed $y$, so taking its negative expectation changes nothing. Hence