Mixture models for capture-recapture count data

The contribution investigates the problem of estimating the size of a population, also known as the missing cases problem. Suppose a registration system is targeting to identify all cases having a certain characteristic such as a specific disease (cancer, heart disease, ...), disease related condition (HIV, heroin use, ...) or a specific behavior (driving a car without license). Every case in such a registration system has a certain notification history in that it might have been identified several times (at least once) which can be understood as a particular capture-recapture situation. Typically, cases are left out which have never been listed at any occasion, and it is this frequency one wants to estimate. In this paper modelling is concentrating on the counting distribution, e.g. the distribution of the variable that counts how often a given case has been identified by the registration system. Besides very simple models like the binomial or Poisson distribution, finite (nonparametric) mixtures of these are considered providing rather flexible modelling tools. Estimation is done using maximum likelihood by means of the EM algorithm. A case study on heroin users in Bangkok in the year 2001 is completing the contribution.

Direct and indirect effects of pathological gambling on risk attitudes

We study individual decision making in a lottery-choice task performed by three different populations: gamblers under psychological treatment ("addicts"), gamblers’ spouses ("victims"), and people who are neither gamblers or gamblers’ spouses ("normals"). We find that addicts are willing to take less risk than normals, but the difference is smaller as a gambler’s time under treatment increases. The large majority of victims report themselves unwilling to take any risk at all. However, addicts in the first year of treatment react more than other addicts to the different values of the risk-return parameter.