Modelling human immunodeficiency virus ribonucleic acid levels with finite mixtures for censored longitudinal data

The measurement of human immunodeficiency virus ribonucleic acid levels over time leads to censored longitudinal data. Suitable models for dynamic modelling of these levels need to take this data characteristic into account. If groups of patients with different developments of the levels over time are suspected the model class of finite mixtures of mixed effects models with censored data is required. We describe the model specification and derive the estimation with a suitable expectation-maximization algorithm. We propose a convenient implementation using closed form formulae for the expected mean and variance of the truncated multivariate distribution. Only efficient evaluation of the cumulative multivariate normal distribution function is required. Model selection as well as methods for inference are discussed. The application is demonstrated on the clinical trial ACTG 315 data.     

On predictive distributions and Bayesian networks

In this paper we are interested in discrete prediction problems for a decision-theoretic setting, where the task is to compute the predictive distribution for a finite set of possible alternatives. This question is first addressed in a general Bayesian framework, where we consider a set of probability distributions defined by some parametric model class. Given a prior distribution on the model parameters and a set of sample data, one possible approach for determining a predictive distribution is to fix the parameters to the instantiation with the maximum a posteriori probability. A more accurate predictive distribution can be obtained by computing the evidence (marginal likelihood), i.e., the integral over all the individual parameter instantiations. As an alternative to these two approaches, we demonstrate how to use Rissanen's new definition of stochastic complexity for determining predictive distributions, and show how the evidence predictive distribution with Jeffrey's prior approaches the new stochastic complexity predictive distribution in the limit with increasing amount of sample data. To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case. In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure. In the empirical part of the paper the predictive distributions are compared by using the simple tree-structured Naive Bayes model, which is used in the experiments for computational reasons. The experimentation with several public domain classification datasets suggest that the evidence approach produces the most accurate predictions in the log-score sense. The evidence-based methods are also quite robust in the sense that they predict surprisingly well even when only a small fraction of the full training set is used.     

Multiple hypothesis tests based On conditional differences in means

Many hypothesis tests are univariate tests and cannot cope with multiple hypothesis without an auxiliary procedure as e. g. the Bonferroni-Holm-procedure. At the same time, there is an urgent need for testing multiple hypothesis due to the very simple existing methods as the Bonferroni-correction or the Bonferroni-Holm-procedure, which suffers from a very small local significance level to detect statistical inferences or the drawback that logical and statistical dependencies among the test statistics are not used, whereby its detection is NP-hard. In honour of this occasion, we present a multiple hypothesis test for i.i.d. random variables based on conditional differences in means, which is capable to cope with multiple hypothesis and does not suffer on such drawbacks as the Bonferroni-correction or the Bonferroni-Holm-procedure. Thereby, the computation time can be neglected.     

In search of family-friendly careers? Professional strategies,work conditions and gender differences in work–family conflict

The aim of this paper is to investigate whether women in a dual-earner context acquire family-friendly jobs as a strategy to keep work–family conflict down. The analysis is based on a survey of newly graduated highly educated men and women in five occupations in Sweden (n?≈?2400). The sample was stratified by occupation and gender to minimize the influence of factors other than gender. The results show that women are more family-oriented, but also more career-oriented than men in their professional strategies. In their jobs, women have less control over work and schedules than men but a similar level of work demands. However, women face lower requirements for employer flexibility (e.g. frequent over time) and this is related to their professional strategies. Finally, women report a higher level of work–family conflict than men in the same occupation, but this gender difference becomes non-significant when accounting for women’s lower level of control. In sum, women in this sample clearly aim for both family and career and do not acquire family-friendly jobs, but aim to avoid ‘family-unfriendly’ requirements for constant availability. To some extent, this enables them to limit their work–family conflict but due to their lower control over work, women still experience more conflict than men in the same occupation.     

The no-wait job shop with regular objective: a method based on optimal job insertion

The no-wait job shop problem (NWJS-R) considered here is a version of the job shop scheduling problem where, for any two operations of a job, a fixed time lag between their starting times is prescribed. Also, sequence-dependent set-up times between consecutive operations on a machine can be present. The problem consists in finding a schedule that minimizes a general regular objective function. We study the so-called optimal job insertion problem in the NWJS-R and prove that this problem is solvable in polynomial time by a very efficient algorithm, generalizing a result we obtained in the case of a makespan objective. We then propose a large neighborhood local search method for the NWJS-R based on the optimal job insertion algorithm and present extensive numerical results that compare favorably with current benchmarks when available.     

‘I Want to Persuade You!’ – Investigating the effectiveness of explicit persuasion concerning attributes of the communicator and the marketing campaign

In explicit persuasion, the communicator states explicitly a desire to persuade the consumer. By referring to an attributional approach, social engagement was simultaneously explored as a beneficial communicator attribute, while cause-related marketing (CRM) was addressed as a boundary condition. In an experiment, we varied the persuasion strategy (explicit vs. implicit), the communicator’s prior experience with social engagement (yes vs. no), and the specific marketing strategy (CRM vs. non-charity marketing). As expected, in the non-charity marketing condition, explicit (vs. implicit) persuasion was more effective when the communicator had prior experience in social engagement. In the CRM condition, explicit (vs. implicit) persuasion was less effective when the communicator had prior experience in social engagement; when no prior experience was reported, persuasiveness increased.     

Compensation or Reinforcement? The Stratification of Parental Responses to Children’s Early Ability

Theory and empirical evidence suggest that parents allocate their investments unequally among their children, thus inducing within-family inequality. We investigate whether parents reinforce or compensate for initial ability differences between their children as well as whether these parental responses vary by family socioeconomic status (SES). Using the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B) and a twin fixed-effects approach to address unobserved heterogeneity, we find that parental responses to early ability differences between their children do vary by family SES. Contrary to prior findings, we find that advantaged parents provide more cognitive stimulation to higher-ability children, and lower-class parents do not respond to ability differences. No analogous stratification in parental responses to birth weight is found, suggesting that parents’ responses vary across domains of child endowments. The reinforcing responses to early ability by high-SES parents do not, however, led to increases in ability differences among children because parental responses have little effect on children’s later cognitive performance in this twin sample.     

The Direct and Indirect Impact of Religion on Well-Being in Germany

A European Social Survey (ESS)—based study of Clark and Lelkes on the European level showed a double positive connection between religion and life-satisfaction: not only did a personal involvement have a positive impact, but there was also a regional externality. Even atheists seemed to be happier in areas with many religious people. However, the regional structure of the ESS can be seen as methodologically doubtful. We therefore replicated the study with more rich German data. In our study we confirm the positive individual effect of religion, but we did find a negative regional externality. However, further analysis revealed, this was an effect of an omitted variable: the degree of urbanization. In a more detailed approach we show that this effect is confined to areas with a protestant majority. We conclude that the positive degree of urbanization influence on life-satisfaction is presumably due to the chance for more political participation in the (protestant) city states in Germany.