An Expert-Based Framework for Probabilistic National Population Projections: The Example of Austria

The traditional way of dealing with uncertainty in population projections through high and low variants is unsatisfactory because it remains unclear what range of uncertainty these alternative paths are assumed to cover. But probabilistic approaches have not yet found their way into official population projections. This paper proposes an expert-based probabilistic approach that seems to meet important criteria for successful application to national and international projections: 1) it provides significant advantages to current practice, 2) it presents an evolution of current practice rather than a discontinuity, 3) it is scientifically sound, and 4) it is applicable to all countries.In a recent Nature article (Lutz et al., 1997) this method was applied to 13 world regions. This paper discusses the applicability to national projections by directly taking the alternative assumptions defined by the Austrian Statistical Office. Sensitivity analyses that resolve some methodological questions about the approach are also presented.     

Comparing relative effects of education and economic resources on infant mortality in developing countries

Research on the social determinants of health has often considered education and economic resources as separate indicators of socioeconomic status. From a policy perspective, however, it is important to understand the relative strength of the effect of these social factors on health outcomes, particularly in developing countries. It is also important to examine not only the impact of education and economic resources of individuals, but also whether community and country levels of these factors affect health outcomes. This analysis uses multilevel regression models to assess the relative effects of education and economic resources on infant mortality at the family, community, and country level using data from demographic and Health Surveys in 43 low-and lower-middle-income countries. We find strong effects for both per capita gross national income and completed secondary education at the country level, but a greater impact of education within families and communities.     

Robust variable selection with application to quality of life research

A large database containing socioeconomic data from 60 communities in Austria and Germany has been built, stemming from 18,000 citizens’ responses to a survey, together with data from official statistical institutes about these communities. This paper describes a procedure for extracting a small set of explanatory variables to explain response variables such as the cognition of quality of life. For better interpretability, the set of explanatory variables needs to be very small and the dependencies among the selected variables need to be low. Due to possible inhomogeneities within the data set, it is further required that the solution is robust to outliers and deviating points. In order to achieve these goals, a robust model selection method, combined with a strategy to reduce the number of selected predictor variables to a necessary minimum, is developed. In addition, this context-sensitive method is applied to obtain responsible factors describing quality of life in communities.     

On- and offline detection of structural breaks in thermal spraying processes

We investigate and develop methods for structural break detection, considering time series from thermal spraying process monitoring. Since engineers induce technical malfunctions during the processes, the time series exhibit structural breaks at known time points, giving us valuable information to conduct the investigations. First, we consider a recently developed robust online (also real-time) filtering (i.e. smoothing) procedure that comprises a test for local linearity. This test rejects when jumps and trend changes are present, so that it can also be useful to detect such structural breaks online. Second, based on the filtering procedure we develop a robust method for the online detection of ongoing trends. We investigate these two methods as to the online detection of structural breaks by simulations and applications to the time series from the manipulated spraying processes. Third, we consider a recently developed fluctuation test for constant variances that can be applied offline, i.e. after the whole time series has been observed, to control the spraying results. Since this test is not reliable when jumps are present in the time series, we suggest data transformation based on filtering and demonstrate that this transformation makes the test applicable.     

A note on fourth moment-based direction dependence measures when regression errors are non normal

Measures of direction dependence enable researchers to determine the directionality of linear effects in bivariate data. Existing fourth moment-based approaches assume that regression errors are at least mesokurtic. Direction dependence measures based on the co-kurtosis of variables are proposed that relax this assumption. Simulations suggest that co-kurtosis-based measures perform equally well as existing kurtosis-based methods when distributional assumptions of the latter are fulfilled. However, kurtosis-based approaches are sensitive to platy- or leptokurtic errors, while co-kurtosis-based measures protect Type I error and power rates. Data requirements necessary for causal inference and recommendations for selecting proper direction dependence measures are discussed.     

Idempotent and multivariate copulas with fractal support

Using special iterated function systems (IFS) Fredricks et al. (2005) constructed two-dimensional copulas with fractal supports and showed that for every s∈(1,2)$s\in (\mathrm{1,2})$ there exists a copula A whose support has Hausdorff dimension s. In the current paper we present a stronger version and prove that the same result holds for the subclass of idempotent copulas. Additionally we show that every doubly stochastic idempotent matrix N (having neither minimum nor maximum rank) induces a family of idempotent copulas such that, firstly, the corresponding Markov kernels transform according to N   and, secondly, the set of Hausdorff dimensions of the supports of elements of the family covers (1,2). Furthermore we generalize the IFS approach to arbitrary dimensions d≥2$d\ge 2$ and show that for every s∈(1,d)$s\in (1,d)$ we can find a d-dimensional copula whose support has Hausdorff dimension s.     

Factors Associated with the Finnish Fertility Decline Since 1776

The analysis of annual age-specific fertility rates in Finland over more than 200 years reveals the existence of a significant early fertility decline at the end of the eighteenth century preceding the secular decline that started around 1910. A reconstruction of age-specific proportions married by a simulation model based on Coale's marriage model indicates that the mean age at marriage increased and the proportion ever-marrying decreased substantially during the period of the early fertility decline. A modification of the index of family limitation applied under certain assumptions to overall fertility rates also indicates that fertility was essentially natural until 1910. Cross-lagged correlation analysis shows that infant mortality does not influence subsequent fertility in the pre-modern period. Finally, a number of socio-economic indicators are related to fertility, and conclusions are drawn from the Finnish case about several hypotheses in the field of demographic transition.