Conflicto,Identificacion y Poder en la Influencia Minoritaria

En 6 condiciones experimentales se presenta a los sujetos un texto minoritario extremamente favorable al aborto, siendo evaluada la influencia en una dimensión directa (actitud frente al aborto) y en una dimensión indirecta (actitud frente a la contracepción). En tres condiciones se introduce una pequeña amenaza y en las otras tres una fuerte amenaza que implica un fuerte costo social simbólico en caso de un eventual acuerdo con la minoría. En cada caso, o bien no se explica ninguna identificación (condiciones control) o bien se lleva a los sujetos a percibirse identificados ya a la Iglesia ya a la minoría. Los resultados principales muestran que a) en las condiciones sin identificación explícita, la minoría obtiene el efecto de conversión esperado por el modelo de la influencia minoritaria, en el caso de que el costo social es elevado; b) por el contrario, en caso de identificación explícita con la minoría el costo social disminuye tanto la influencia indirecta como la directa, lo que aboga en defensa del modelo del poder; c) en las condiciones en las que los sujetos son identificados con la Iglesia aparece un curioso efecto: cuanto mayor es el costo social más se acercan los sujetos a la minoría. Dado que las explicaciones divergen según la interpretación que los autores dan de este efecto paradójico y del conjunto de los resultados, se presentan dos interpretaciones complementarias, incluso aunque se contradigan entre ellas.     

Optimal collapsing of mixture distributions in robust recursive estimation

Several authors have discussed Kalman filtering procedures using a mixture of normals as a model for the distributions of the noise in the observation and/or the state space equations. Under this model, resulting posteriors involve a mixture of normal distributions, and a “collapsing method” must be found in order to keep the recursive procedure simple. We prove that the Kullback-Leibler distance between the mixture posterior and that of a single normal distribution is minimized when we choose the mean and variance of the single normal distribution to be the mean and variance of the mixture posterior. Hence, “collapsing by moments” is optimal in this sense. We then develop the resulting optimal algorithm for “Kalman filtering” for this situation, and illustrate its performance with an example.     

ROC curve and covariates: extending induced methodology to the non-parametric framework

Continuous diagnostic tests are often used to discriminate between diseased and healthy populations. The receiver operating characteristic (ROC) curve is a widely used tool that provides a graphical visualisation of the effectiveness of such tests. The potential performance of the tests in terms of distinguishing diseased from healthy people may be strongly influenced by covariates, and a variety of regression methods for adjusting ROC curves has been developed. Until now, these methodologies have assumed that covariate effects have parametric forms, but in this paper we extend the induced methodology by allowing for arbitrary non-parametric effects of a continuous covariate. To this end, local polynomial kernel smoothers are used in the estimation procedure. Our method allows for covariate effect not only on the mean, but also on the variance of the diagnostic test. We also present a bootstrap-based method for testing for a significant covariate effect on the ROC curve. To illustrate the method, endocrine data were analysed with the aim of assessing the performance of anthropometry for predicting clusters of cardiovascular risk factors in an adult population in Galicia (NW Spain), duly adjusted for age. The proposed methodology has proved useful for providing age-specific thresholds for anthropometric measures in the Galician community.     

Reports of Conferences,Institutes, and Seminars: Crossroads to the Future

Reports cover the 2006 Ohio Valley Group of Technical Services Librarians Conference and the 2006 North American Serials Interest Group Conference.     

Influence Measures Based on Cressie-Read Divergence Measures in Multivariate Linear Model

We define a new family of influence measures based on the divergence measures, in the multivariate general linear model. Influence measures are obtained by quantifying the divergence between the sample distribution of an estimate obtained with all the observations and the sample distribution of the same estimate obtained without any observation. This approach is applied to best linear unbiased estimates of estimable functions. Therefore, these diagnostics can be applied to every statistical multivariate technique that can be formulated like this kind of model. Some examples are considered to clarify the applicability of the introduced diagnostics.     

Describing extra-binomial variation with partially correlated models

Data in the form of proportions are often analyzed under a binomial model. However, because genuine random sampling is often infeasible, the subjects in the sample may be collected in clumps and the variances of the observed proportions may be considerably larger than those corresponding to the binomial model. A set of data from a study of the proportion of subjects testing positive to the disease toxoplasmosis is used in this article to motivate partially correlated binomial models capable of describing data observed in practical situations where clumped sampling is likely to appear, According to these models, the extra-binomial variance of the observed frequencies may range from a linear to a quadratic function of the sample size. An efficient algorithm for the evaluation of the resulting probability mass function is given.     

Recursive characterization of a large family of discrete probability distributions showing extra-Poisson variation

This article analyses the broad family of discrete probability distributions generated by relating Prob (y) to Prob (y?1), …, Prob (y?n), for some n≥1, through a recursive equation. This family contains the binomial, negative binomial and Poisson distributions as well as the Katz family of distributions. In addition, the suggested family contains some convolutions of Poisson distributions and other generalized distributions, which provide models for Poisson overdispersion or underdispersion.     

A Bayesian Analysis of Directional Data Using the von Mises–Fisher Distribution

ABSTRACT

This article presents a Bayesian analysis of the von Mises–Fisher distribution, which is the most important distribution in the analysis of directional data. We obtain samples from the posterior distribution using a sampling-importance-resampling method. The procedure is illustrated using simulated data as well as real data sets previously analyzed in the literature.     

Comparing Nonparametric Estimators for Length-Biased Data

Length-biased data appear when sampling lifetimes by cross-section. Right-censoring may affect the sampled information due to time limitation in following-up, lost to follow-up cases, etc. In this article, we compare by simulations two alternative nonparametric estimators of the lifetime distribution function when the data are length-biased and right-censored. These estimates, recently introduced in the literature, are based on nonparametric maximum-likelihood and moment-based principles. It is shown that the relative benefits associated to each estimator depend on several factors, such as the shape of the underlying distribution, sample size, or censoring level.     

Sensitivity analysis in multivariate methods: Decomposition of an arbitrary influence into a finite number of components

The influence function of the covariance matrix is decomposed into a finite number of components. This decomposition provides a useful tool to develop efficient methods for computing empirical influence curves related to various multivariate methods. It can also be used to characterize multivariate methods from the sensitivity perspective. A numerical example is given to demonstrate efficient computing and to characterize some procedures of exploratory factor analysis.