Perceptual identification of talker ethnicity in Vancouver English

Studies of ethnolinguistic variation typically begin by describing the speech production variables used to index social groups. In this study, we begin with indexical recognition – the perceptual identification of speakers’ self‐identified ethnic groups – to determine whether speakers produce ethnolinguistic variation and whether listeners are sensitive to it. Speech samples were recorded from thirty individuals from Metro Vancouver who self‐identified as Chinese, East Indian, or White Canadian. These utterances were used in a perception task where listeners categorized speakers’ ethnicities. Listeners’ social networks were labeled according to the ethnic group with which they reported spending the most time. Analyses indicate that while speakers vary in their productive expression of ethnolinguistic variation, listeners are consistent in their labeling choices. Listener accuracy was higher for voices from the listeners’ reported social group and White voices. These results suggest that familiarity with ethnic groups through social networks and mainstream culture influences indexical recognition.     

Investigating important interactions between water and food security for child health in Burkina Faso

Failures in either water systems or food systems, or a combination of system failures, could provide the underlying explanation for continued high levels of malnutrition in many regions. We focus on child health and offer the first spatially explicit analysis of the interaction between water source and food insecurity on children’s health in Burkina Faso, an African nation that continues to struggle with poor children’s health. We combine data from the 2010 Demographic and Health Survey, a small USAID water quality survey collected from community wells, and remotely sensed imagery. Results suggest that, in a few cases, reliable and clean water sources are positively correlated to children’s linear growth and weight gain, although in many regions, the interaction with community-level food production is critical to understanding health outcomes. The results also suggest that maternal health and nutrition during pregnancy and breastfeeding are foundational to the healthy development of young children. In all, the findings provide evidence of the importance of multi-sectoral interventions targeted at improving children’s health.     

Hybrid and Size‐Corrected Subsampling Methods

This paper considers inference in a broad class of nonregular models. The models considered are nonregular in the sense that standard test statistics have asymptotic distributions that are discontinuous in some parameters. It is shown in Andrews and Guggenberger (2009a) that standard fixed critical value, subsampling, and m out of n bootstrap methods often have incorrect asymptotic size in such models. This paper introduces general methods of constructing tests and confidence intervals that have correct asymptotic size. In particular, we consider a hybrid subsampling/fixed‐critical‐value method and size‐correction methods. The paper discusses two examples in detail. They are (i) confidence intervals in an autoregressive model with a root that may be close to unity and conditional heteroskedasticity of unknown form and (ii) tests and confidence intervals based on a post‐conservative model selection estimator.     

The robustness of chain block designs and coat-of-mail designs

Chain block designs are relatively vulnerable to loss of information when missing values or outliers may occur. An alternative class of designs, coat-of-mail designs, are proposed and the relative robustness of the two types of design are compared.     

Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions

The last decade has seen an explosion of work on the use of mixture models for clustering. The use of the Gaussian mixture model has been common practice, with constraints sometimes imposed upon the component covariance matrices to give families of mixture models. Similar approaches have also been applied, albeit with less fecundity, to classification and discriminant analysis. In this paper, we begin with an introduction to model-based clustering and a succinct account of the state-of-the-art. We then put forth a novel family of mixture models wherein each component is modeled using a multivariate t-distribution with an eigen-decomposed covariance structure. This family, which is largely a t-analogue of the well-known MCLUST family, is known as the tEIGEN family. The efficacy of this family for clustering, classification, and discriminant analysis is illustrated with both real and simulated data. The performance of this family is compared to its Gaussian counterpart on three real data sets.     

Book review

Review of OPTIMAL CONTROL, EXPECTATIONS AND UNCERTAINTY by Sean Holly and Andrew Hughes Hallett by Scott David Hakala Southern Methodist Univ., Dept. of Economics, Dallas, Tx 75275.     

Consistent Moment Selection Procedures for Generalized Method of Moments Estimation

This paper considers a generalized method of moments (GMM) estimation problem in which one has a vector of moment conditions, some of which are correct and some incorrect. The paper introduces several procedures for consistently selecting the correct moment conditions. The procedures also can consistently determine whether there is a sufficient number of correct moment conditions to identify the unknown parameters of interest. The paper specifies moment selection criteria that are GMM analogues of the widely used BIC and AIC model selection criteria. (The latter is not consistent.) The paper also considers downward and upward testing procedures. All of the moment selection procedures discussed in this paper are based on the minimized values of the GMM criterion function for different vectors of moment conditions. The procedures are applicable in time-series and cross-sectional contexts. Application of the results of the paper to instrumental variables estimation problems yields consistent procedures for selecting instrumental variables.