A class of improved heteroskedasticity-consistent covariance matrix estimators

The heteroskedasticity-consistent covariance matrix estimator proposed by White (1980), also known as HC0, is commonly used in practical applications and is implemented into a number of statistical software. Cribari–Neto, Ferrari & Cordeiro (2000) have developed a bias-adjustment scheme that delivers bias-corrected White estimators. There are several variants of the original White estimator that also commonly used by practitioners. These include the HC1, HC2 and HC3 estimators, which have proven to have superior small-sample behavior relative to White’s estimator. This paper defines a general bias-correction mechamism that can be applied not only to White’s estimator, but to variants of this estimator as well, such as HC1, HC2 and HC3. Numerical evidence on the usefulness of the proposed corrections is also presented. Overall, the results favor the sequence of improved HC2 estimators.

Developmental class coalitions: historical experiences and prospects

Este artigo discute o conceito de coalizões de classe como uma alternativa parcial à luta de classes na compreensão das sociedades capitalistas; define duas coalizões de classes básicas - a desenvolvimentista e a liberal; apresenta brevemente três coalizões de classe desenvolvimentistas paradigmáticas - a mercantilista, a bismarckiana, e a social-democrata (ou dos anos dourados do capitalismo); e usa esse arcabouço teórico para entender o capitalismo contemporâneo nos anos pós neoliberais - os anos que se seguem a crise financeira global de 2008

A retrospective look at esthetic resin composite and glass-ionomer Class III restorations: A 2-year clinical evaluation

Objective: A restorative material for Class III cavities must, besides being functional, be esthetically satisfactory, providing good working conditions and several shade and color options. A clinical evaluation was initiated to compare the suitability of resin composite and glass-ionomer cement materials for such restorations.Method and materials: Forty-two Class III conservative cavities, esthetically important because of facial extensions, were selected. Resin composite restorations were placed in 21 cavities, and the remaining 21 were restored with glass-ionomer cement. The following characteristics were studied: color or-esthetics, anatomic shape, surface texture, staining, marginal infiltration, dental plaque retention, and occurrence of fracture. After 24 months, the restorations were evaluated.Results: the only statistically significant difference between the resin composite and glass-ionomer cement restorations in the experimental period involved color or esthetics.Conclusion: Resin composites and glass-ionomer materials provide excellent functional and esthetic results in Class III cavities when properly indicated.

The restructuring of labour in Brazil in the 2000s: flexible work and the precarisation of working life

This article argues that the precarisation of employment that has taken place in Brazil since the 1990s has been fundamentally different in kind from earlier forms of precariousness, which took place outside the formal economy. The new forms of precariousness are taking place within the sphere of the economy controlled by transnational corporations. Although they have only reached critical mass during the 2000s, the ground was prepared by ‘post neoliberal’ restructuring, including labour law reforms, that took place in Brazil during the 1990s and introduced new forms of flexible working. The article argues that the new condition of labour now emerging in Brazil, which is a structural feature of labour under global capitalism, is characterised by psychosocial dynamics that cause: first, class desubjectivation; second, a ‘seizure’ of the waged worker's subjectivity; and third, the reduction of living labour to the status of a workforce treated as goods. Comprehending these changes necessitates a related change in the theoretical and methodological framework in which the precariousness of work is studied, one that incorporates within its scope the issues of workers' health and the quality of working life.

Sleep length, working hours and socio-demographic variables are associated with time attending evening classes among working college students

It is the aim of the present study to assess factors associated with time spent in class among working college students. Eighty-two working students from 21 to 26 years old participated in this study. They were enrolled in an evening course of the University of Sao Paulo, Brazil. Participants answered a questionnaire on living and working conditions. During seven consecutive days, they wore an actigraph, filled out daily activity diaries (including time spent in classes) and the Karolinska Sleepiness Scale every three hours from waking until bedtime. Linear regression analyses were performed in order to assess the variables associated with time spent in classes. The results showed that gender, sleep length, excessive sleepiness, alcoholic beverage consumption (during workdays) and working hours were associated factors with time spent in class. Thus, those who spent less time in class were males, slept longer hours, reported excessive sleepiness on Saturdays, worked longer hours, and reported alcohol consumption. The combined effects of long work hours (>40 h/week) and reduced sleep length may affect lifestyles and academic performance. Future studies should aim to look at adverse health effects induced by reduced sleep duration, even among working students who spent more time attending evening classes.

Squared Extrapolation Methods (SQUAREM): A New Class of Simple and Efficient Numerical Schemes for Accelerating the Convergence of the EM Algorithm

We derive a new class of iterative schemes for accelerating the convergence of the EM algorithm, by exploiting the connection between fixed point iterations and extrapolation methods. First, we present a general formulation of one-step iterative schemes, which are obtained by cycling with the extrapolation methods. We, then square the one-step schemes to obtain the new class of methods, which we call SQUAREM. Squaring a one-step iterative scheme is simply applying it twice within each cycle of the extrapolation method. Here we focus on the first order or rank-one extrapolation methods for two reasons, (1) simplicity, and (2) computational efficiency. In particular, we study two first order extrapolation methods, the reduced rank extrapolation (RRE1) and minimal polynomial extrapolation (MPE1). The convergence of the new schemes, both one-step and squared, is non-monotonic with respect to the residual norm. The first order one-step and SQUAREM schemes are linearly convergent, like the EM algorithm but they have a faster rate of convergence. We demonstrate, through five different examples, the effectiveness of the first order SQUAREM schemes, SqRRE1 and SqMPE1, in accelerating the EM algorithm. The SQUAREM schemes are also shown to be vastly superior to their one-step counterparts, RRE1 and MPE1, in terms of computational efficiency. The proposed extrapolation schemes can fail due to the numerical problems of stagnation and near breakdown. We have developed a new hybrid iterative scheme that combines the RRE1 and MPE1 schemes in such a manner that it overcomes both stagnation and near breakdown. The squared first order hybrid scheme, SqHyb1, emerges as the iterative scheme of choice based on our numerical experiments. It combines the fast convergence of the SqMPE1, while avoiding near breakdowns, with the stability of SqRRE1, while avoiding stagnations. The SQUAREM methods can be incorporated very easily into an existing EM algorithm. They only require the basic EM step for their implementation and do not require any other auxiliary quantities such as the complete data log likelihood, and its gradient or hessian. They are an attractive option in problems with a very large number of parameters, and in problems where the statistical model is complex, the EM algorithm is slow and each EM step is computationally demanding.

Checking Assumptions in Latent Class Regression Models via a Markov Chain Monte Carlo Estimation Approach: An Application to Depression and Socio-Economic Status

Latent class regression models are useful tools for assessing associations between covariates and latent variables. However, evaluation of key model assumptions cannot be performed using methods from standard regression models due to the unobserved nature of latent outcome variables. This paper presents graphical diagnostic tools to evaluate whether or not latent class regression models adhere to standard assumptions of the model: conditional independence and non-differential measurement. An integral part of these methods is the use of a Markov Chain Monte Carlo estimation procedure. Unlike standard maximum likelihood implementations for latent class regression model estimation, the MCMC approach allows us to calculate posterior distributions and point estimates of any functions of parameters. It is this convenience that allows us to provide the diagnostic methods that we introduce. As a motivating example we present an analysis focusing on the association between depression and socioeconomic status, using data from the Epidemiologic Catchment Area study. We consider a latent class regression analysis investigating the association between depression and socioeconomic status measures, where the latent variable depression is regressed on education and income indicators, in addition to age, gender, and marital status variables. While the fitted latent class regression model yields interesting results, the model parameters are found to be invalid due to the violation of model assumptions. The violation of these assumptions is clearly identified by the presented diagnostic plots. These methods can be applied to standard latent class and latent class regression models, and the general principle can be extended to evaluate model assumptions in other types of models.

Multilevel Latent Class Models with Dirichlet Mixing Distribution

Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when either the number of classes or the cluster size is large. We propose a maximum pairwise likelihood (MPL) approach via a modified EM algorithm for this case. We also show that a simple latent class analysis, combined with robust standard errors, provides another consistent, robust, but less efficient inferential procedure. Simulation studies suggest that the three methods work well in finite samples, and that the MPL estimates often enjoy comparable precision as the ML estimates. We apply our methods to the analysis of comorbid symptoms in the Obsessive Compulsive Disorder study. Our models' random effects structure has more straightforward interpretation than those of competing methods, thus should usefully augment tools available for latent class analysis of multilevel data.

Student Performance in Traditional vs. Online Format: Evidence from an MBA Level Introductory Economics Class

All previous studies comparing online and face-to-face format for instruction of economics compared courses that were either online or face-to-face format and regressed exam scores on selected student characteristics. This approach is subject to the econometric problems of self-selection omitted unobserved variables. Our study uses two methods to deal with these problems. First we eliminate self-selection bias by using students from a course that uses both instruction formats. Second, we use the exam questions as the unit of observation, and eliminate omitted variable bias by using an indicator variable for each student to capture the effect of differences in unobserved student characteristics on learning outcomes. We report the finding that students had a significantly greater chance of answering a question correctly if it came from a chapter covered online.

The urban middle class in the instability of new democracies

The recent revolts of the middle class in the national capitals of the Philippines and Thailand have raised a new question about democratic consolidation. Why would the urban middle class, which is expected to stabilize democracy, expel the democratically elected leaders through extra-constitutional action? This article seeks to explain such middle class deviation from democratic institutions through an examination of urban primacy and the change in the winning coalition. The authoritarian regime previously in power tended to give considerable favor to the primate city to prevent it revolting against the ruler, because it could have become a menace to his power. But after democratization the new administration shifts policy orientation from an urban to rural bias because it needs to garner support from rural voters to win elections. Such a shift dissatisfies the middle class in the primate city. In this article I take up the Philippines as a case study to examine this theory.