Remittance Activity among Brazilians in the US and Canada

This comparative study uses data collected in 1990 and 1991 to examine the remittance behavior of Brazilians who had recently arrived in Canada and the United States. These data permit an examination of remittance activity among immigrants relatively soon after their arrival in a pair of host destinations. Prior to contrasting the remittance activity of these newly arrived immigrants, we first document the high degree of similarity between the two groups at their time of arrival; a point that becomes important when contrasting their divergent outcomes. Next, this study contributes to the research literature on micro‐level remittance patterns and behaviors by focusing on three policy relevant dependant variables. More specifically, multivariate analyses are undertaken to examine those individual‐level factors that best determine: (1) who remits, (2) how much they remit, and (3) when funds are remitted for productive purposes. Because the data analyzed were collected with the same instrument, results are then contrasted for the two destinations. One major finding to emerge from this cross‐national study was that even immigrants who are extremely similar on all socio‐economic measures at time of arrival may soon manifest divergent outcomes due to their distinctive country of destination experiences. Consequently, although some common predictors were found in both locales, there were far fewer than expected. For example, family obligation variables were usually significant in the predicted directions for the United States data, while this was almost never the case for the Canadian data. Furthermore, some variables often assumed to predict remittance behavior were insignificant in both locations. This study concludes by considering possible explanations for these results, as well as discussing the need for additional theoretical work and data collection in the area of immigrant remittance activity.     

Demos-Ethos: a framework to study the Icelandic and Basque cases through Critical Social Innovation and Applied Ethics

This article presents a framework informed by two disciplines, Social Innovation and Applied Ethics, to be used in an ongoing analysis of territorial democratic systems. It aims to combine Social Innovation and Applied Ethics analysis to better understand the socio-political regeneration process in Iceland initiated with the 2008 global crisis. This crisis, particularly in Iceland had an economic and political aspect affecting the self-understanding of the territories as a whole, but in the Basque Country at present, it also deals with the current peace process to settle down political violence and its causes. In both cases there are deep underlying value issues. Nevertheless, this article introduces only the results obtained in Iceland. While its democratic system has proven to be able to contest the causes of the crisis, there is no definitive evidence that the re-examination process that was opened in October 2008 is resolved yet.     

Advances on a Decision Analytic Approach to Exposure-Based Chemical Prioritization

The volume and variety of manufactured chemicals is increasing, although little is known about the risks associated with the frequency and extent of human exposure to most chemicals. The EPA and the recent signing of the Lautenberg Act have both signaled the need for high-throughput methods to characterize and screen chemicals based on exposure potential, such that more comprehensive toxicity research can be informed. Prior work of Mitchell et al. using multicriteria decision analysis tools to prioritize chemicals for further research is enhanced here, resulting in a high-level chemical prioritization tool for risk-based screening. Reliable exposure information is a key gap in currently available engineering analytics to support predictive environmental and health risk assessments. An elicitation with 32 experts informed relative prioritization of risks from chemical properties and human use factors, and the values for each chemical associated with each metric were approximated with data from EPA's CP_CAT database. Three different versions of the model were evaluated using distinct weight profiles, resulting in three different ranked chemical prioritizations with only a small degree of variation across weight profiles. Future work will aim to include greater input from human factors experts and better define qualitative metrics.     

A Lagrangian bound for many-to-many assignment problems

A simple procedure to tighten the Lagrangian bounds is proposed. The approach is interpreted in two ways. First, it can be seen as a reformulation of the original problem aimed to split the resulting Lagrangian problem into two subproblems. Second, it can be considered as a search for a tighter estimation of the penalty term arising in the Lagrangian problem. The new bounds are illustrated by a small example and studied numerically for a class of the generalized assignment problems.     

ON CONVERGENCE RATES FOR NONPARAMETRIC POSTERIOR DISTRIBUTIONS

Rates of convergence of Bayesian nonparametric procedures are expressed as the maximum between two rates: one is determined via suitable measures of concentration of the prior around the “true” density f₀, and the other is related to the way the mass is spread outside a neighborhood of f₀. Here we provide a lower bound for the former in terms of the usual notion of prior concentration and in terms of an alternative definition of prior concentration. Moreover, we determine the latter for two important classes of priors: the infinite–dimensional exponential family, and the Pólya trees.     

Linear latent structure analysis: Mixture distribution models with linear constraints

A new method for analyzing high-dimensional categorical data, Linear Latent Structure (LLS) analysis, is presented. LLS models belong to the family of latent structure models, which are mixture distribution models constrained to satisfy the local independence assumption. LLS analysis explicitly considers a family of mixed distributions as a linear space, and LLS models are obtained by imposing linear constraints on the mixing distribution.LLS models are identifiable under modest conditions and are consistently estimable. A remarkable feature of LLS analysis is the existence of a high-performance numerical algorithm, which reduces parameter estimation to a sequence of linear algebra problems. Simulation experiments with a prototype of the algorithm demonstrated a good quality of restoration of model parameters.     

Conditions equivalent and doubly equivalent to consistency of approximate MLE's

Summary Strasser (1981) introduced approximately maximum likelihood estimators (AMLE's) and found a condition equivalent to strong consistency of all AMLE's. Here a condition weaker than that of Strasser is proved to be equivalent to the usual consistency of all AMLE's. Under an additional regularity this condition is shown to be doubly equivalent, which means that it is equivalent to consistency, and its contrary is equivalent to inconsistency of all AMLE's. The doubly equivalent conditions are important—we present an example where MLE is strongly consistent but some AMLE's are inconsistent. It is proved that the additional regularity can be reduced to the finiteness of an observations entropy. All results are motivate and illustrated by examples. Supported by CSAS grant N. 17503.     

Decomposable pseudodistances and applications in statistical estimation

The aim of this paper is to introduce new statistical criteria for estimation, suitable for inference in models with common continuous support. This proposal is in the direct line of a renewed interest for divergence based inference tools imbedding the most classical ones, such as maximum likelihood, Chi-square or Kullback–Leibler. General pseudodistances with decomposable structure are considered, they allowing defining minimum pseudodistance estimators, without using nonparametric density estimators. A special class of pseudodistances indexed by α>0$\alpha >0$, leading for α↓0$\alpha \downarrow 0$ to the Kullback–Leibler divergence, is presented in detail. Corresponding estimation criteria are developed and asymptotic properties are studied. The estimation method is then extended to regression models. Finally, some examples based on Monte Carlo simulations are discussed.