Interval-censored data with misclassification: a Bayesian approach

Survival data involving silent events are often subject to interval censoring (the event is known to occur within a time interval) and classification errors if a test with no perfect sensitivity and specificity is applied. Considering the nature of this data plays an important role in estimating the time distribution until the occurrence of the event. In this context, we incorporate validation subsets into the parametric proportional hazard model, and show that this additional data, combined with Bayesian inference, compensate the lack of knowledge about test sensitivity and specificity improving the parameter estimates. The proposed model is evaluated through simulation studies, and Bayesian analysis is conducted within a Gibbs sampling procedure. The posterior estimates obtained under validation subset models present lower bias and standard deviation compared to the scenario with no validation subset or the model that assumes perfect sensitivity and specificity. Finally, we illustrate the usefulness of the new methodology with an analysis of real data about HIV acquisition in female sex workers that have been discussed in the literature.     

Linear censored regression models with skew scale mixtures of normal distributions

A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable. Such incomplete observation of the response variable is called censoring. Censorship can occur for a variety of reasons, including limitations of measurement equipment, design of the experiment, and non-occurrence of the event of interest until the end of the study. In the presence of censoring, the dependence of the response variable on the explanatory variables can be explored through regression analysis. In this paper, we propose to examine the censorship problem in context of the class of asymmetric, i.e., we have proposed a linear regression model with censored responses based on skew scale mixtures of normal distributions. We develop a Monte Carlo EM (MCEM) algorithm to perform maximum likelihood inference of the parameters in the proposed linear censored regression models with skew scale mixtures of normal distributions. The MCEM algorithm has been discussed with an emphasis on the skew-normal, skew Student-t-normal, skew-slash and skew-contaminated normal distributions. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset.     

Recombinative generalization: an exploratory study in musical reading

The present study aimed to extend the findings of recombinative generalization research in alphabetical reading and spelling to the context of musical reading. One participant was taught to respond discriminatively to six two-note sequences, choosing the corresponding notation on the staff in the presence of each sequence. When novel three- and four-note sequences were presented, she selected the corresponding notation. These results suggest the generality of previous research to the context of musical teaching.     

An introduction: the predicament of Brazilian culture

‘Truth’ and ideology (as error or falsity), like any other oppositional terms, take up the same productive powers and necessarily track each other very closely. Not much is necessary for any statement to move from the former into the latter field. My review of the main twentieth‐century lines of Brazilian racial studies, in this introduction, traces how they have moved miscegenation and racial democracy back and forth across the border between social scientific ‘truth’ and racial ideology. Because the papers included in this issue, rather than repeating this move, address how these socio‐historical signifiers inform the contemporary Brazilian social configuration, they move beyond the predicament shared by both narratives of the nation and social scientific accounts of racial subjection in Brazil.     

Towards a Critique of the Socio-logos of Justice: The Analytics of Raciality and the Production of Universality

Fear of police terror has long been a daily facet of the lives of economically dispossessed people of colour in the urban spaces of North America, Europe, Latin America, and elsewhere. This essay addresses the conditions of possibility of the form of race injustice manifested in racial profiling and police brutality. It challenges the centrality of the logic of exclusion - the view that race is only politically and socially significant when race identification is explicitly or implicitly used to justify discrimination - in the understanding of race injustice. It explores the political-symbolic processes that have produced the mechanisms of racial power of which police brutality is a most dramatic example. To elaborate this critique of the logic of exclusion, I discuss the newspaper account of an episode of police terror in a favela of Rio de Janeiro, Brazil. What the examination of the newspaper coverage of the Chacina de Vigário Geral reveals, is that this form of race injustice indicated the operation of meanings produced in an analytic of raciality , encompassing the various instances of manufacturing the modern concept of the racial - the science of life , the science of man , and the sociology of race relations.     

Chisquared and related inducing pivot variables: an application to orthogonal mixed models

Abstract

We use chi-squared and related pivot variables to induce probability measures for model parameters, obtaining some results that will be useful on the induced densities. As illustration we considered mixed models with balanced cross nesting and used the algebraic structure to derive confidence intervals for the variance components. A numerical application is presented.