Bound and exact methods for assessing link vulnerability in complex networks

Assessing network systems for failures is critical to mitigate the risk and develop proactive responses. In this paper, we investigate devastating consequences of link failures in networks. We propose an exact algorithm and a spectral lower-bound on the minimum number of removed links to incur a significant level of disruption. Our exact solution can identify optimal solutions in both uniform and weighted networks through solving a well-constructed mixed integer program. Also, our spectral lower-bound derives from the Laplacian eigenvalues an estimation on the vulnerability of large networks that are intractable for exact methods. Through experiments on both synthetic and real-world networks, we demonstrate the efficiency of the proposed methods.     

Leadership and Stress Orientations of Germans: An Examination Based on Gender, Age, and Government Work Experience

Today??s working adults often display different leadership orientations, as well as moderate to severe levels of stress depending on the situation and various demographic traits. In order to explore the stress, task orientation and relationship orientation variables of German people, this study examined the differences of 232 respondents in Germany based on their gender, age, and public sector work experience. The results showed that respondents had dissimilar scores on their leadership orientations. Younger respondents had significantly lower task and relationship orientation scores. Furthermore, females reported having a significantly higher level of stress perception. These findings are useful for expatriate managers and professionals who work with German-born workers. Besides, relevant literature on the German culture, suggestions and implications for future studies are presented.     

Partial covariate adjusted regression

Covariate adjusted regression (CAR) is a recently proposed adjustment method for regression analysis where both the response and predictors are not directly observed [?entürk, D., Müller, H.G., 2005. Covariate adjusted regression. Biometrika 92, 75–89]. The available data have been distorted by unknown functions of an observable confounding covariate. CAR provides consistent estimators for the coefficients of the regression between the variables of interest, adjusted for the confounder. We develop a broader class of partial covariate adjusted regression (PCAR) models to accommodate both distorted and undistorted (adjusted/unadjusted) predictors. The PCAR model allows for unadjusted predictors, such as age, gender and demographic variables, which are common in the analysis of biomedical and epidemiological data. The available estimation and inference procedures for CAR are shown to be invalid for the proposed PCAR model. We propose new estimators and develop new inference tools for the more general PCAR setting. In particular, we establish the asymptotic normality of the proposed estimators and propose consistent estimators of their asymptotic variances. Finite sample properties of the proposed estimators are investigated using simulation studies and the method is also illustrated with a Pima Indians diabetes data set.     

A New Family of BAN Estimators for Polytomous Logistic Regression Models based on ϕ- Divergence Measures

In this paper we study polytomous logistic regression model and the asymptotic properties of the minimum ϕ-divergence estimators for this model. A simulation study is conducted to analyze the behavior of these estimators as function of the power-divergence measure ϕ_(λ) Research partially done when was visiting the Bowling Green State University as the Distinguished Lukacs Professor     

Biodiversity,coastal protection and resource endowment: Policy options for improving ocean health

The paper develops a production function for the Global Ocean Health Index (OHI) for 2013. Data from the Ocean Health Statistics, plus from the Human Development Index (HDI) for 151 countries are used. We employ two-stage regression model to conduct this evaluation. The Tobit model, used to obtain the estimated dependent variable, results show Coastal Protection, Livelihoods and Economies, Tourism and Recreation, Iconic Species, Clean Water and Biodiversity, Food Provision, Artisanal Fisheries Opportunities, Natural Products, and Carbon Storage are significant variables. The rank regression in the second stage showed that HDI and Marine Protected Areas (MPAs) significantly influenced the predicted value of the OHI. Policy makers should note that biodiversity increases have the greatest effect on OHI, and its improvement is within reach of even the poorest country. Countries with varying levels of resource endowment may choose different techniques to improve OHI, but the implementation of MPAs should be priority.