Salinization of the Bangladesh Delta worsens economic precarity

Population and Environment - Anthropogenic environmental changes are having complex effects on all aspects of the hydrological cycle. In estuarine areas, these factors are coalescing...     

Minimum-segment convex drawings of 3-connected cubic plane graphs

A convex drawing of a plane graph G is a plane drawing of G, where each vertex is drawn as a point, each edge is drawn as a straight line segment and each face is drawn as a convex polygon. A maximal segment is a drawing of a maximal set of edges that form a straight line segment. A minimum-segment convex drawing of G is a convex drawing of G where the number of maximal segments is the minimum among all possible convex drawings of G. In this paper, we present a linear-time algorithm to obtain a minimum-segment convex drawing Γ of a 3-connected cubic plane graph G of n vertices, where the drawing is not a grid drawing. We also give a linear-time algorithm to obtain a convex grid drawing of G on an $(\frac{n}{2}+1)\times(\frac {n}{2}+1)$ grid with at most s _n +1 maximal segments, where $s_{n}=\frac{n}{2}+3$ is the lower bound on the number of maximal segments in a convex drawing of G.     

Negotiating identities: the lives of Pakistani and Bangladeshi young disabled people

Research has generally amalgamated minority ethnic (all called 'Asian' or 'black') disabled young people's experiences and failed to acknowledge the multiple aspects of Asian and black disabled identities, for example how the combined attributes of race, ethnicity, religion, gender, culture, class and disability shape their perspectives and experiences. In an attempt to address this issue my doctoral research explored the experiences and perspectives of 13 young Pakistani and Bangladeshi disabled people. By drawing on the substantive and theoretical findings which emerged from my analysis in this paper I shall consider how multiple aspects of identity, such as ethnicity, disability and gender, affect this population's identity and self-image and how this makes their experiences different from white disabled young people and other minority groups' experiences.     

Inference in multivariate linear regression models with elliptically distributed errors

In this study we investigate the problem of estimation and testing of hypotheses in multivariate linear regression models when the errors involved are assumed to be non-normally distributed. We consider the class of heavy-tailed distributions for this purpose. Although our method is applicable for any distribution in this class, we take the multivariate t-distribution for illustration. This distribution has applications in many fields of applied research such as Economics, Business, and Finance. For estimation purpose, we use the modified maximum likelihood method in order to get the so-called modified maximum likelihood estimates that are obtained in a closed form. We show that these estimates are substantially more efficient than least-square estimates. They are also found to be robust to reasonable deviations from the assumed distribution and also many data anomalies such as the presence of outliers in the sample, etc. We further provide test statistics for testing the relevant hypothesis regarding the regression coefficients.     

Stringency-based ranking of normality tests

Many parametric statistical inferential procedures in finite samples depend crucially on the underlying normal distribution assumption. Dozens of normality tests are available in the literature to test the hypothesis of normality. Availability of such a large number of normality tests has generated a large number of simulation studies to find a best test but no one arrived at a definite answer as all depends critically on the alternative distributions which cannot be specified. A new framework, based on stringency concept, is devised to evaluate the performance of the existing normality tests. Mixture of t-distributions is used to generate the alternative space. The LR-tests, based on Neyman–Pearson Lemma, have been computed to construct a power envelope for calculating the stringencies of the selected normality tests. While evaluating the stringencies, Anderson–Darling (AD) statistic turns out to be the best normality test.     

Temptation and Revealed Preference

Gul and Pesendorfer (2001) model the static behavior of an agent who ranks menus prior to the experience of temptation. This paper models the dynamic behavior of an agent whose ranking of menus itself is subject to temptation. The representation for the agent's dynamically inconsistent choice behavior views him as possessing a dynamically consistent view of what choices he “should” make (a normative preference) and being tempted by menus that contain tempting alternatives. Foundations for the model require a departure from Gul and Pesendorfer's idea that temptation creates a preference for commitment. Instead, it is hypothesized that distancing an agent from the consequences of his choices separates normative preference and temptation.