Latino Group Consciousness and Perceptions of Commonality with African Americans*

Objective. Currently, Latinos and African Americans constitute more than one‐quarter of the U.S. population. The sheer size of these groups suggests an opportunity for increased political influence, with this opportunity providing the incentive for greater social and political interaction between them. The objective of this article is to determine the role of Latino group consciousness in the formation of attitudes toward African Americans. Methods. Utilizing data from the 1999 Washington Post/Henry J. Kaiser Family Foundation National Survey on Latinos, a multivariate ordered logit model is employed to test the relationship between Latino group consciousness and perceptions of commonality with African Americans. Results. Results show that group consciousness in the form of Latino internal commonality and perceived discrimination are contributors to Latino perceptions of commonality with African Americans. Conclusion. This analysis demonstrates that before any meaningful political alliances can be formed between the nation's two largest minority groups, Latinos may need to develop strong levels of panethnic identity.     

The resilient frugivorous fauna of an urban forest fragment and its potential role in vegetation enrichment

Urban Ecosystems - Anthropocentric defaunation affects critical ecological processes, such as seed dispersal, putting ecosystems and biomes at risk, and leading to habitat impoverishment. Diverse...     

Histogram estimation of radon-nikodym derivatives for strong mixing data

Nonparametric inference for point processes is discussed by way of histograms, which provide a nice tool for the analysis of on-line data. The construction of histograms depends on a sequence of partitions, which we take tc be nonenibedded to allow partitions with sets of equal measure. This presents some theoretical problems, which are addressed with an assumption on the decomposition of second order moments. In another direction, we drop the usual independence assumption on the sample, replacing it by a strong mixing assumption. Under this setting, we study the convergence of the histogram in probability, which depends on approximation conditions between the distributions of random pairs and the product of their marginal distributions, and^almost completely, which is based on the decomposition of the second order moments. This last convergence is stated on two versions according to the assumption of Laplace transforms or the Cramer moment conditions. These are somewhat stronger, but enable us to recover the usual condition on the decrease rate of sets on each partition. In the final section we prove that the finite dimensional distributions converge in distribution to a Gaussian centered vector with a specified covariance.     

The Features of Development in the Pacific Countries of the African,Caribbean and Pacific Group

In this article we present a new proposal for the measurement of development, applied to the Pacific Countries of the African, Caribbean and Pacific Group (ACP), conditional on their insularity, and with privileged relations with the European Union. Our index has been constructed attending to the criteria defined in the Goals of the Millennium Declaration. For this purpose we will use the P₂ distance method for the year 2007, the last year for which data are available. This index integrates socio-economic variables that permit a territorial ordering of the Pacific ACPs, in terms of those partial indicators.     

On risk-averse maximum weighted subgraph problems

In this work, we consider a class of risk-averse maximum weighted subgraph problems (R-MWSP). Namely, assuming that each vertex of the graph is associated with a stochastic weight, such that the joint distribution is known, the goal is to obtain a subgraph of minimum risk satisfying a given hereditary property. We employ a stochastic programming framework that is based on the formalism of modern theory of risk measures in order to find minimum-risk hereditary structures in graphs with stochastic vertex weights. The introduced form of risk function for measuring the risk of subgraphs ensures that optimal solutions of R-MWS problems represent maximal subgraphs. A graph-based branch-and-bound (BnB) algorithm for solving the proposed problems is developed and illustrated on a special case of risk-averse maximum weighted clique problem. Numerical experiments on randomly generated Erdös-Rényi graphs demonstrate the computational performance of the developed BnB.     

A Bayesian analysis of directional data using the projected normal distribution

This paper presents a Bayesian analysis of the projected normal distribution, which is a flexible and useful distribution for the analysis of directional data. We obtain samples from the posterior distribution using the Gibbs sampler after the introduction of suitably chosen latent variables. The procedure is illustrated using simulated data as well as a real data set previously analysed in the literature.     

Level-dependent deforestation trajectories in the Brazilian Amazon from 1970 to 2001

This article demonstrates the importance of examining level-dependent deforestation trajectories that may be undetected within regional analyses. We examine Amazon-wide, sub-regional, settlement, and farm-level deforestation trajectories in the Brazilian Amazon and suggest factors that underlie level-dependent differences in spatial patterns and temporal magnitudes of deforestation from 1970 to 2001. At a sub-regional level, we find significant variation in frequency, magnitude, and intensity of deforestation associated with context-specific processes, including areas that have stopped deforesting, are rapidly reforesting, while at the same time increasing economic value of forest-based production. We use remote-sensing data from 1970 to 2001. For Amazon-wide and state-level analyses, we use data available from Brazil??s National Institute for Space Research. For sub-regional, settlement, and farm-lot levels, time-series Landsat images and aerial photographs and detailed field-based research are used to reconstruct the history of deforestation. Our analysis empirically demonstrates that understanding deforestation trajectories requires differentiating underlying causes at different levels of analysis and to be wary of overarching explanations and solutions that ignore differences in scale.