Individualism vs Collectivism in Different Cultures: A cross-cultural study

In this study, individualism vs collectivism, in line with the work of Triandis in 1985, was assessed in two groups of male and female students, in Egypt and Germany. The results confirm the effect that cultural background has on individualist vs collectivist orientations in both of these cultures for male and female students. Men and women scored higher on individualism in Germany than in Egypt, whereas collectivism scores were higher in Egypt than in Germany. These findings are discussed in terms of general recommendations for intercultural interventions that discourage viewing people solely in terms of group membership, and instead as distinct individuals.     

Mission,money, and merit: Strategic decision making by nonprofit managers

Public and nonprofit organizations need to make strategic choices about where to invest their resources. They also need to expose hidden managerial assumptions and lack of adequate knowledge that prevent the attainment of consensus in strategic decision making. The approach we developed and tested in the field used a dynamic, three‐dimensional model that tracks individual programs in an organization's portfolio on their contribution to mission, money, and merit. The first dimension measures whether the organization is doing the right things; the second, whether it is doing things right financially; and the third, whether it doing things right in terms of quality. Senior managers provide their own evaluations of the organization's programs. Both the consensus view and the variation in individual assessments contribute to an improved managerial understanding of the organization's current situation and to richer discussions in strategic decision making. In field tests, this visual model proved to be a useful and powerful tool for illuminating underlying assumptions and variations in knowledge among managers facing the complex, multidimensional tradeoffs needed in strategic decision making.     

Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

Goodness‐of‐fit methods for matched case‐control studies

The authors propose graphical and numerical methods for checking the adequacy of the logistic regression model for matched case‐control data. Their approach is based on the cumulative sum of residuals over the covariate or linear predictor. Under the assumed model, the cumulative residual process converges weakly to a centered Gaussian limit whose distribution can be approximated via computer simulation. The observed cumulative residual pattern can then be compared both visually and analytically to a certain number of simulated realizations of the approximate limiting process under the null hypothesis. The proposed techniques allow one to check the functional form of each covariate, the logistic link function as well as the overall model adequacy. The authors assess the performance of the proposed methods through simulation studies and illustrate them using data from a cardiovascular study.     

Three-level main-effects designs exploiting prior information about model uncertainty

To explore the projection efficiency of a design, Tsai, et al [2000. Projective three-level main effects designs robust to model uncertainty. Biometrika 87, 467–475] introduced the Q criterion to compare three-level main-effects designs for quantitative factors that allow the consideration of interactions in addition to main effects. In this paper, we extend their method and focus on the case in which experimenters have some prior knowledge, in advance of running the experiment, about the probabilities of effects being non-negligible. A criterion which incorporates experimenters’ prior beliefs about the importance of each effect is introduced to compare orthogonal, or nearly orthogonal, main effects designs with robustness to interactions as a secondary consideration. We show that this criterion, exploiting prior information about model uncertainty, can lead to more appropriate designs reflecting experimenters’ prior beliefs.     

Statistical consulting

The paper and the special issue focus on the activity of statistical consulting and its varieties. This includes academic consulting, consulting to and in industry as well as statistics in public media.     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.