Principal point classification: Applications to differentiating drug and placebo responses in longitudinal studies

Principal points are cluster means for theoretical distributions. A discriminant methodology based on principal points is introduced. The principal point classification method is useful in clinical trials where the goal is to distinguish and differentiate between different treatment effects. Particularly, in psychiatric studies where placebo response rates can be very high, the principal point classification is illustrated to distinguish specific drug responders from non-specific placebo responders.     

Polarization in the Reaction to Health‐Risk Information: A Question of Social Position?

Dissemination of risk information is ubiquitous in contemporary society. We explore how individuals react in everyday life to health-risk information, based on what they report in personal interviews. Health-risk information was without exception recognized as unstable and inconsistent. This conformity, however, did not extend to the narratives regarding how health-risk information should be handled. Two opposite positions (ideal-typical strategies) are presented. Either you tend to process and evaluate new information or you tend to ignore it as a whole. Our attempt to reveal the underlying rationality in these two very different approaches involved the exploration of three different avenues of interpretation and brings together two scientific paradigms--economics and sociology--that provide the framework for our analysis. First, we suggest that a greater long-term experience of explicit choice implies that this kind of action becomes more natural and less resource consuming, whereas a reliance on habits in daily life--a natural adjustment to a lack of resources--makes it is more costly to bother about new information. Second, with fewer resources in the short run, fewer opportunities to mitigate bad outcomes, and greater exposure to social and material risks, one is less likely to devote resources to deal with health-risk information. Third, there are several possible links between a low propensity to take account of risk information and a high relative importance of genuine uncertainty in one's life. These theoretical perspectives provide a viable set of hypotheses regarding mechanisms that may contribute to social differences in the response to health-risk information.     

Relationships between bullying, psychosocial work environment and individual stress reactions

It is frequently assumed that a poor psychosocial working environment will create conditions that encourage bullying. However, few studies have examined this assumption while comparing work environment ratings of bullied and non-bullied employees who work in the same organization and/or department. The objectives of this study were therefore, first, to investigate relationships between bullying and other psychosocial work environment factors within a particular organizational setting and, second, to investigate if bullied employees reported higher levels of stress than non-bullied employees. A total of 186 blue-collar employees from a Danish manufacturing company participated in the study (response rate of 93%). Results showed significant differences in bullied and non-bullied employees' ratings of psychosocial factors such as job control, management style, role clarity, social climate, social contact and work centrality. In order to investigate assumptions that a poor psychosocial work environment creates conditions that encourage bullying, a number of analyses compared the company's manufacturing departments with either high, medium or low levels of bullying. Results showed significant departmental differences. Yet, when bullied employees were removed from the statistical analyses, with regard to the perceived psychosocial environment these same departments could be differentiated only on the basis of rated job demands and management style. The results therefore bring into question the assumption that a generally poor work environment contributes to bullying. None the less, they do suggest that management style may directly or indirectly have contributed to a higher level of bullying. Also, bullied employees reported significantly more symptoms of psychological stress and mental fatigue than non-bullied employees.     

A robust approach to longitudinal data analysis

The author introduces robust techniques for estimation, inference and variable selection in the analysis of longitudinal data. She first addresses the problem of the robust estimation of the regression and nuisance parameters, for which she derives the asymptotic distribution. She uses weighted estimating equations to build robust quasi‐likelihood functions. These functions are then used to construct a class of test statistics for variable selection. She derives the limiting distribution of these tests and shows its robustness properties in terms of stability of the asymptotic level and power under contamination. An application to a real data set allows her to illustrate the benefits of a robust analysis.     

Ensuring Pareto Optimality by Referendum Voting

We consider a society confronting the decision of accepting or rejecting a list of (at least two) proposals. Assuming separability of preferences, we show the impossibility of guaranteeing Pareto optimal outcomes through anonymous referendum voting, except in the case of an odd number of voters confronting precisely two proposals. In this special case, majority voting is the only anonymous social choice rule which guarantees Pareto optimal referendum outcomes.     

Sufficient Reduction in Multivariate Surveillance

The relation between change points in multivariate surveillance is important but seldom considered. The sufficiency principle is here used to clarify the structure of some problems, to find efficient methods, and to determine appropriate evaluation metrics. We study processes where the changes occur simultaneously or with known time lags. The surveillance of spatial data is one example where known time lags can be of interest. A general version of a theorem for the sufficient reduction of processes that change with known time lags is given. A simulation study illustrates the benefits or the methods based on the sufficient statistics.     

Clustering the mixed panel dataset using Gower's distance and k-prototypes algorithms

ABSTRACT

Panel datasets have been increasingly used in economics to analyze complex economic phenomena. Panel data is a two-dimensional array that combines cross-sectional and time series data. Through constructing a panel data matrix, the clustering method is applied to panel data analysis. This method solves the heterogeneity question of the dependent variable, which belongs to panel data, before the analysis. Clustering is a widely used statistical tool in determining subsets in a given dataset. In this article, we present that the mixed panel dataset is clustered by agglomerative hierarchical algorithms based on Gower's distance and by k-prototypes. The performance of these algorithms has been studied on panel data with mixed numerical and categorical features. The effectiveness of these algorithms is compared by using cluster accuracy. An experimental analysis is illustrated on a real dataset using Stata and R package software.     

A new test for the extreme value distribution

In this paper a test statistic which is a modification of the W statistic for testing the goodness of fit for the two paremeter extreme value (smallest element) distribution is proposed. The test statistic Is obtained as the ratio of two linear estimates of the scale parameter. It Is shown that the suggested statistic is computationally simple and has good power properties. Percentage points of the statistic are obtained by performing Monte Carlo experiments. An example is given to illustrate the test procedure.