Auswirkungen von Macht auf das ��berleben in Extremsituationen: Ein Vergleich der Titanic und Lusitania Schiffskatastrophen

Comparing the Titanic and Lusitania disaster the impact of financial and physical power as well as the emergence of social norms on survival during life-and-death situations is analyzed. Despite the fact that some maritime disasters have become famous, a quantitative understanding of survival in life-and-death situations is still lacking. In a quasi-natural experiment multivariate probit estimations are conducted based on publicly available data. The paper suggests that in life-and-death situations differences in time restrictions are crucial. When time is scarce individual self-interested fight-or-flight behavior will predominate and result in a stronger competition for survival. In contrast, altruism and adherence to social norms emerge when there is sufficient time to reflect on the event and circumstances: Social norms require time to evolve, and they loose against physical strength in a shorter window of opportunity.     

Debating Muslims

Michael M.J. Fischer and Mehdi Abedi. Debating Muslims: cultural dialogue in postmodernity and tradition. Madison: University of Wisconsin Press. 1990. xxxvi, 564pp., plates, 3 appendices, chronology of events, notes, glossary, bibliography. $36.95 ISBN 0 299 12430 4 (hard), 0 299 12434 7 (paper).     

Locally quadratic log likelihood and data-based transformations

The investigation of multi-parameter likelihood functions is simplified if the log likelihood is quadratic near the maximum, as then normal approximations to the likelihood can be accurately used to obtain quantities such as likelihood regions. This paper proposes that data-based transformations of the parameters can be employed to make the log likelihood more quadratic, and illustrates the method with one of the simplest bivariate likelihoods, the normal two-parameter likelihood.     

Polynomials for classification trees and applications

This paper relates computational commutative algebra to tree classification with binary covariates. With a single classification variable, properties of uniqueness of a tree polynomial are established. In a binary multivariate context, it is shown how trees for many response variables can be made into a single ideal of polynomials for computations. Finally, a new sequential algorithm is proposed for uniform conditional sampling. The algorithm combines the lexicographic Groebner basis with importance sampling and it can be used for conditional comparisons of regulatory network maps. The binary state space leads to an explicit form for the design ideal, which leads to useful radical and extension properties that play a role in the algorithms.     

A clustering approach to interpretable principal components

A new method for constructing interpretable principal components is proposed. The method first clusters the variables, and then interpretable (sparse) components are constructed from the correlation matrices of the clustered variables. For the first step of the method, a new weighted-variances method for clustering variables is proposed. It reflects the nature of the problem that the interpretable components should maximize the explained variance and thus provide sparse dimension reduction. An important feature of the new clustering procedure is that the optimal number of clusters (and components) can be determined in a non-subjective manner. The new method is illustrated using well-known simulated and real data sets. It clearly outperforms many existing methods for sparse principal component analysis in terms of both explained variance and sparseness.     

Modeling Endogenous Mobility in Earnings Determination

ABSTRACT

We evaluate the bias from endogenous job mobility in fixed-effects estimates of worker- and firm-specific earnings heterogeneity using longitudinally linked employer–employee data from the LEHD infrastructure file system of the U.S. Census Bureau. First, we propose two new residual diagnostic tests of the assumption that mobility is exogenous to unmodeled determinants of earnings. Both tests reject exogenous mobility. We relax exogenous mobility by modeling the matched data as an evolving bipartite graph using a Bayesian latent-type framework. Our results suggest that allowing endogenous mobility increases the variation in earnings explained by individual heterogeneity and reduces the proportion due to employer and match effects. To assess external validity, we match our estimates of the wage components to out-of-sample estimates of revenue per worker. The mobility-bias-corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates. Supplementary materials for this article are available online.     

Relative Risk Regression for Binary Outcomes: Methods and Recommendations

Relative risks are often considered preferable to odds ratios for quantifying the association between a predictor and a binary outcome. Relative risk regression is an alternative to logistic regression where the parameters are relative risks rather than odds ratios. It uses a log link binomial generalised linear model, or log‐binomial model, which requires parameter constraints to prevent probabilities from exceeding 1. This leads to numerical problems with standard approaches for finding the maximum likelihood estimate (MLE), such as Fisher scoring, and has motivated various non‐MLE approaches. In this paper we discuss the roles of the MLE and its main competitors for relative risk regression. It is argued that reliable alternatives to Fisher scoring mean that numerical issues are no longer a motivation for non‐MLE methods. Nonetheless, non‐MLE methods may be worthwhile for other reasons and we evaluate this possibility for alternatives within a class of quasi‐likelihood methods. The MLE obtained using a reliable computational method is recommended, but this approach requires bootstrapping when estimates are on the parameter space boundary. If convenience is paramount, then quasi‐likelihood estimation can be a good alternative, although parameter constraints may be violated. Sensitivity to model misspecification and outliers is also discussed along with recommendations and priorities for future research.