Past exam of the mathematics course of the University of Cambridge / 2023 / iii / Paper 207 / 2 / c

For each substudy define the log-odds treatment effectThe normal random-effects log-likelihood, up to an additive constant, isIts score equations giveThese are the maximum-likelihood estimators rather than the unbiased sample-variance estimator. At an interior solution with ${\hat{\sigma }}^2>0$, the Hessian in $(\mu ,{\sigma }^2)$ is negative definite, which is the required second-order condition.

Given numerical values of $\mu$ and ${\sigma }^2$, maximize the joint log-likelihood over the response rates $0<p_{jk}<1$:This is a penalized binomial regression problem and can be solved by Newton method or another numerical optimizer. One may alternate this maximization with the closed-form updates for $\mu$ and ${\sigma }^2$ from part i until convergence. The selected solution should have a negative-definite Hessian in the fitted log-odds parameters.