Dual systems estimation is a capture–recapture approach to population estimation restricted to two captures of the population. When applied to human populations, the population captures may be existing incomplete listings of the population, as provided by census and administrative datasets. In contrast to much capture-recapture analysis, dual systems applications in official statistics are usually concerned with estimating the distribution of a human population, over combinations of covariates, such as age, sex, ethnic group and small geographic area, rather than focussing primarily on the total population count. We synthesise theory and methods for Bayesian dual systems estimation for this problem, which we refer to as small domain population estimation. We primarily work within a model framework that combines a general model for the covariate distribution, such as an unrestricted multinomial in the case of categorical covariates, with hierarchical logistic models for the probability of inclusion on the lists. We explore the issue of dependence between the two lists which leads to a well-known identifiability problem. We illustrate the use of informative priors for the degree of dependence expressed as an odds ratio, or for certain aggregate level population totals. Although progress can be made using informative priors, the underlying identifiability problem means that inferences are sensitive to prior choices. We relate our approach to the popular log-linear modelling approach to capture-recapture analysis and note that the latter is primarily a re-parameterisation of our model set-up, though with a specific implied prior for the total population in the case of Poisson log-linear models.

The mixture cure model in survival analysis has received large and growing attention in the last few decades. Restricting ourselves mainly to the one-sample case, we present here an overview drawing together some recent significant advances and earlier results, and pointing out areas where further work is needed. New results presented include a discussion of testing for the presence of long term survivors in the null case (when there are no cures present), the probability that an individual is cured (when cures are present), and further analysis of the idea of sufficient followup. Extreme value methods play a key role. We draw attention to some challenging open problems.

The maximum likelihood estimator (MLE) for the unknown parameter vector in logistic regression is well known to be biased. There are many different approaches to reduce this bias including bias correction, adjustment of the score function or of the data itself, jackknifing, penalizing the likelihood, exact logistic regression, and the discriminant function approach. These approaches, as well as many different simulation studies comparing them, are reviewed here. Since the studies use very different parameter settings and sometimes contradict each other, no general recommendations can be given. However, most studies find that the bias of the MLE is substantial for small to medium samples, that the bias-corrected estimators tend to overcorrect in very small samples, and that Firth’s estimator, when considered, is the best choice.