A New Bayesian Nonparametric Mixture Model

We propose a new mixture model for Bayesian nonparametric inference. Rather than considering extensions from current approaches, such as the mixture of Dirichlet process model, we end up shrinking it, by making the weights less complex. We demonstrate the model and discuss its performance.     

Accuracy of Power-Divergence Statistics for Testing Independence and Homogeneity in Two-Way Contingency Tables

The small-sample accuracy of seven members of the family of power-divergence statistics for testing independence or homogeneity in contingency tables was studied via simulation. The likelihood ratio statistic G ² and Pearson's X ² statistic are among these seven members, whose behavior was studied at nominal test sizes of.01 and.05 with marginal distributions that could be uniform or skewed and with a set of sample sizes that included sparseness conditions as measured through table density (i.e., the ratio of sample size to number of cells). The likelihood ratio statistic G ² rejected the null hypothesis too often even with large table density, whereas Pearson's X ² was sufficiently accurate and only presented a minor misbehavior when table density was less than two observations/cell. None of the other five statistics outperformed Pearson's X ². A nonasymptotic variant of X ² solved the minor inaccuracies of Pearson's X ² and turned out to be the most accurate statistic for testing independence or homogeneity, even with table densities of one observation/cell. These results clearly advise against the use of the likelihood ratio statistic G ².     

A modification to some proposed tests in relation to the problem of switching regression models

Several tests proposed to deal with the problem that a switch occurs in a regression model are discussed and an attempt Is made to improve them through a very natural modification, The modified tests are Illustrated using data given by Quandt where a switch is present     

Linguistic competence/language access services (LAS) in end-of-life and palliative care: a social work leadership imperative

Despite the mandated and moral imperative to advance linguistic competence in all health care contexts, leadership that addresses this area of practice and study in end-of-life and palliative care is not readily obvious. Because social work is ideally suited to lead efforts to advance linguistically-accessible end-of-life and palliative care, social workers are encouraged to assume the challenge. This article focuses on topics that are elementary yet central to discussions on language diversity and leadership initiatives to advance language access in end-of-life and palliative care contexts: importance and function of language, extent of language diversity, inequity related to language diversity, mandates and standards related to language access, and approaches and competencies that contribute positively to language access.     

Testing the Personal Wellbeing Index on 12–16?Year-Old Adolescents in 3 Different Countries with 2 New Items

The 7-item adult version of the Personal Wellbeing scale (Cummins et al. Social Indic Res 64:159?C190, 2003) was administered to two samples of adolescents aged 12?C16 in Brazil (N?=?1,588) and Spain (N?=?2,900), and to a sample of adolescents aged 14?C16 in Chile (N?=?843). The results obtained were analyzed to determine its psychometric characteristics when used with adolescents in the three different countries and to check whether two additional items would improve its qualities. Results reveal that the new PWI-9 version worked well with the adolescents in the three countries, improving some of the qualities of the PWI-7. One of the added items, satisfaction with oneself, appears to be a major contributor to unique explained variance when regressed on the single-item of overall life satisfaction (OLS). The model we present using structural equations shows good fit statistics for the factor structure, with both 7 and 9 items. Separate in-country analyses demonstrate that cultural context has a strong influence on correlations and saturations between the studied variables and also on the explained variance. Probably related to this fact, the Model fit structure is good in Brazil (with a low PWI variance accounted for by its predictors) and Spain (medium), but rather modest in Chile, where data show a high proportion of the PWI variance accounted for by its predictors. However, a multi-group factor analysis among the three countries restricting saturations to 1 in each country in order to make data comparable across countries still show a good fit of the proposed model for both PWI-7 and PWI-9.     

Income Inequality Indices Interpreted as Measures of Relative Deprivation/Satisfaction

This paper considers different ways of making comparisons between individuals in terms of deprivation and/or satisfaction. This allows the Gini index, the Bonferroni index and the De Vergottini index to be interpreted as social deprivation measures as well as social satisfaction measures. The inequality measures that belong to the ?? family, or linear combinations of them, are obtained when using different weighting schemes to average the deprivation and satisfaction associated with each income level. Particularly, the generalised Gini indices (Yitzhaki, Int Econ Rev 24:617?C628 in 1983), the indices proposed by Aaberge (J Econ Inequal 5(3):305?C322, 2007) or those proposed by Imedio-Olmedo et al. (J Public Econ Theory 13(1):97?C124, 2011) can be used to evaluate social deprivation or social satisfaction in an income distribution.     

Optimal designs for treatment comparisons represented by graphs

Consider an experiment for comparing a set of treatments: in each trial, one treatment is chosen and its effect determines the mean response of the trial. We examine the optimal approximate designs for the estimation of a system of treatment contrasts under this model. These designs can be used to provide optimal treatment proportions in more general models with nuisance effects. For any system of pairwise treatment comparisons, we propose to represent such a system by a graph. Then, we represent the designs by the inverses of the vertex weights in the corresponding graph and we show that the values of the eigenvalue-based optimality criteria can be expressed using the Laplacians of the vertex-weighted graphs. We provide a graph theoretic interpretation of D-, A- and E-optimality for estimating sets of pairwise comparisons. We apply the obtained graph representation to provide optimality results for these criteria as well as for ’symmetric’ systems of treatment contrasts.     

Clustering and classification problems in genetics through U-statistics

ABSTRACT

Genetic data are frequently categorical and have complex dependence structures that are not always well understood. For this reason, clustering and classification based on genetic data, while highly relevant, are challenging statistical problems. Here we consider a versatile U-statistics-based approach for non-parametric clustering that allows for an unconventional way of solving these problems. In this paper we propose a statistical test to assess group homogeneity taking into account multiple testing issues and a clustering algorithm based on dissimilarities within and between groups that highly speeds up the homogeneity test. We also propose a test to verify classification significance of a sample in one of two groups. We present Monte Carlo simulations that evaluate size and power of the proposed tests under different scenarios. Finally, the methodology is applied to three different genetic data sets: global human genetic diversity, breast tumour gene expression and Dengue virus serotypes. These applications showcase this statistical framework's ability to answer diverse biological questions in the high dimension low sample size scenario while adapting to the specificities of the different datatypes.     

Asymmetric matrix-valued covariances for multivariate random fields on spheres

ABSTRACT

Matrix-valued covariance functions are crucial to geostatistical modelling of multivariate spatial data. The classical assumption of symmetry of a multivariate covariance function is overly restrictive and has been considered as unrealistic for most of the real data applications. Despite of that, the literature on asymmetric covariance functions has been very sparse. In particular, there is some work related to asymmetric covariances on Euclidean spaces, depending on the Euclidean distance. However, for data collected over large portions of planet Earth, the most natural spatial domain is a sphere, with the corresponding geodesic distance being the natural metric. In this work, we propose a strategy based on spatial rotations to generate asymmetric covariances for multivariate random fields on the d-dimensional unit sphere. We illustrate through simulations as well as real data analysis that our proposal allows to achieve improvements in the predictive performance in comparison to the symmetric counterpart.