Examination and visualisation of the simplifying assumption for vine copulas in three dimensions

Vine copulas are a highly flexible class of dependence models, which are based on the decomposition of the density into bivariate building blocks. For applications one usually makes the simplifying assumption that copulas of conditional distributions are independent of the variables on which they are conditioned. However this assumption has been criticised for being too restrictive. We examine both simplified and non‐simplified vine copulas in three dimensions and investigate conceptual differences. We show and compare contour surfaces of three‐dimensional vine copula models, which prove to be much more informative than the contour lines of the bivariate marginals. Our investigation shows that non‐simplified vine copulas can exhibit arbitrarily irregular shapes, whereas simplified vine copulas appear to be smooth extrapolations of their bivariate margins to three dimensions. In addition to a variety of constructed examples, we also investigate a three‐dimensional subset of the well‐known uranium data set and visually detect the fact that a non‐simplified vine copula is necessary to capture its complex dependence structure.     

Maximum expected entropy transformed Latin hypercube designs

Existing projection designs (e.g. maximum projection designs) attempt to achieve good space-filling properties in all projections. However, when using a Gaussian process (GP), model-based design criteria such as the entropy criterion is more appropriate. We employ the entropy criterion averaged over a set of projections, called expected entropy criterion (EEC), to generate projection designs. We show that maximum EEC designs are invariant to monotonic transformations of the response, i.e. they are optimal for a wide class of stochastic process models. We also demonstrate that transformation of each column of a Latin hypercube design (LHD) based on a monotonic function can substantially improve the EEC. Two types of input transformations are considered: a quantile function of a symmetric Beta distribution chosen to optimize the EEC, and a nonparametric transformation corresponding to the quantile function of a symmetric density chosen to optimize the EEC. Numerical studies show that the proposed transformations of the LHD are efficient and effective for building robust maximum EEC designs. These designs give projections with markedly higher entropies and lower maximum prediction variances (MPV''s) at the cost of small increases in average prediction variances (APV''s) compared to state-of-the-art space-filling designs over wide ranges of covariance parameter values.     

Globale organisation — globale manager — globale Klasse?

The globalization of business corporations led to the thesis that a so-called global management class would emerge, both integrating transnational companies and facilitating a global business culture which is able to turn cultural diversity into a competitive advantage. Two questions stem from this thesis: First, how do talents and high potentials in transnational companies become global managers? Second, is global management to be understood as an integrating culture or as a selective one in the context of transnational corporations? The article explores these questions by contrasting the theory of global management with empirical findings from research concerning the strategies of international management assignment and the forming of expatriates’ “mental memberships“. It is shown that global management rests on a ‘thin’ Ethos of economic rationalities which give way to an exclusive “business monoculture” challenging cultural diversity taking place in everyday-life of transnational organizations.     

Estimation of a linear model under microaggregation by individual ranking

Summary Microaggregation by individual ranking is one of themost commonly applied disclosure control techniques for continuous microdata. The paper studies the effect of microaggregation by individual ranking on the least squares estimation of a multiple linear regression model. It is shown that the traditional least squares estimates are asymptotically unbiased. Moreover, the least squares estimates asymptotically have the same variances as the least squares estimates based on the original (non-aggregated) data. Thus, asymptotically, microaggregation by individual ranking does not result in a loss of efficiency in the least squares estimation of a multiple linear regression model. I thank Hans Schneeweiss for very helpful discussions and comments. Financial support from the Deutsche Forschungsgemeinschaft (German Science Foundation) is gratefully acknowledged.     

Exploring Strategy Implementation Consistency Over Time: The Moderating Effects of Industry Velocity and Firm Performance

In this paper we empirically investigate the temporal development of a firm’s strategy implementation consistency (SIC), i.e. the alignment between firms’ resource allocation decisions (RAD) and their articulated corporate concept (Noda and Bower, 1996; Burgelman and Grove, 1996; Love et al., 2002; Mintzberg, 1978). Doing so, we test whether (1) SIC is more likely to increase or decline over time, (2) whether firms competing in (low) high-velocity environments in fact show different temporal patterns in SIC, and (3) whether overperforming firms succeed in conserving their level of SIC. For our analysis we draw on 6238 RAD of 20 publicly listed firms with European origin over a period of 4–6 years. Applying maximum likelihood ordered logit estimation, our results indicate that the likelihood of an alignment of RAD and a firm’s corporate concept decreases over time. In line with scholars’ perception of high-velocity environments, we find that the firms in our sample competing under such conditions show no clear trend in SIC. These firms tend to “zig-zag” over time – swaying off and pulling back to their strategic course independent of the timing of the announcement of a corporate concept. We also find that overperforming firms are unsuccessful in preserving their SIC at the same level over time. Based on the empirical findings the paper discusses implications for theory and derives suggestions for corporate level managers on how to balance SIC and strategic flexibility.     

The therapeutic misconception: a threat to valid parental consent for pediatric neuroimaging research

Neuroimaging research has brought major advances to child health and wellbeing. However, because of the vulnerabilities associated with neurological and developmental conditions, the parental need for hope, and the expectation of parents that new medical advances can benefit their child, pediatric neuroimaging research presents significant challenges to the general problem of consent in the context of research involving children. A particular challenge in this domain is created by the presence of therapeutic misconception on the part of parents and other key research stakeholders. This article revierws the concept of therapeutic misconception and its role in pediatric neuroimaging research. It argues that this misconception can compromise consent given by parents for the involvement of their children in research as healthy controls or as persons with neurological and developmental conditions. The article further contends that therapeutic misconception can undermine the research ethics review process for proposed and ongoing neuroimaging studies. Against this backdrop, the article concludes with recommendations for mitigating the effects of therapeutic misconception in pediatric neuroimaging research.     

The cost of not knowing the radius

Robust Statistics considers the quality of statistical decisions in the presence of deviations from the ideal model, where deviations are modelled by neighborhoods of a certain size about the ideal model. We introduce a new concept of optimality (radius-minimaxity) if this size or radius is not precisely known: for this notion, we determine the increase of the maximum risk over the minimax risk in the case that the optimally robust estimator for the false neighborhood radius is used. The maximum increase of the relative risk is minimized in the case that the radius is known only to belong to some interval [r _l ,r _u ]. We pursue this minmax approach for a number of ideal models and a variety of neighborhoods. Also, the effect of increasing parameter dimension is studied for these models. The minimax increase of relative risk in case the radius is completely unknown, compared with that of the most robust procedure, is 18.1% versus 57.1% and 50.5% versus 172.1% for one-dimensional location and scale, respectively, and less than 1/3 in other typical contamination models. In most models considered so far, the radius needs to be specified only up to a factor , in order to keep the increase of relative risk below 12.5%, provided that the radius–minimax robust estimator is employed. The least favorable radii leading to the radius–minimax estimators turn out small: 5–6% contamination, at sample size 100.