Refugees and transnationalism on the Thai–Burmese border

The past decade has seen the rapid development of transnationalism research, but transnationalism from below in situations of mass refugee influx has received little attention. However, the case study of Burmese refugees in Thailand clearly demonstrates that those refugees can maintain economic, social, cultural and political links with co‐nationals in all the domains of the refugee diaspora, even if their capabilities are in principle strained. It is argued here that the legal status of the person or diaspora organization concerned, as well as the country of origin and the host country have a larger influence on the type of transnationalism than the label ‘migrant’ or ‘refugee’. The concept of transnationalism should thus be conceived in a more encompassing sense, both geographically, thematically and including all emigrants regardless of their original motivations for migration. The article is based on fieldwork, including over 150 interviews with Burmese refugees and political activists.     

Polynomial Histograms for Multivariate Density and Mode Estimation

Abstract. We consider the problem of efficiently estimating multivariate densities and their modes for moderate dimensions and an abundance of data. We propose polynomial histograms to solve this estimation problem. We present first‐ and second‐order polynomial histogram estimators for a general d‐dimensional setting. Our theoretical results include pointwise bias and variance of these estimators, their asymptotic mean integrated square error (AMISE), and optimal binwidth. The asymptotic performance of the first‐order estimator matches that of the kernel density estimator, while the second order has the faster rate of O(n^?6/(d+6)). For a bivariate normal setting, we present explicit expressions for the AMISE constants which show the much larger binwidths of the second order estimator and hence also more efficient computations of multivariate densities. We apply polynomial histogram estimators to real data from biotechnology and find the number and location of modes in such data.     

Near Optimal Prediction from Relevant Components

Abstract. The random x regression model is approached through the group of rotations of the eigenvectors for the x ‐covariance matrix together with scale transformations for each of the corresponding regression coefficients. The partial least squares model can be constructed from the orbits of this group. A generalization of Pitman's Theorem says that the best equivariant estimator under a group is given by the Bayes estimator with the group's invariant measure as the prior. A straightforward application of this theorem turns out to be impossible since the relevant invariant prior leads to a non‐defined posterior. Nevertheless we can devise an approximate scale group with a proper invariant prior leading to a well‐defined posterior distribution with a finite mean. This Bayes estimator is explored using Markov chain Monte Carlo technique. The estimator seems to require heavy computations, but can be argued to have several nice properties. It is also a valid estimator when p>n.