Joint models for mixed categorical outcomes: a study of HIV risk perception and disease status in Mozambique

Two types of bivariate models for categorical response variables are introduced to deal with special categories such as ‘unsure’ or ‘unknown’ in combination with other ordinal categories, while taking additional hierarchical data structures into account. The latter is achieved by the use of different covariance structures for a trivariate random effect. The models are applied to data from the INSIDA survey, where interest goes to the effect of covariates on the association between HIV risk perception (quadrinomial with an ‘unknown risk’ category) and HIV infection status (binary). The final model combines continuation-ratio with cumulative link logits for the risk perception, together with partly correlated and partly shared trivariate random effects for the household level. The results indicate that only age has a significant effect on the association between HIV risk perception and infection status. The proposed models may be useful in various fields of application such as social and biomedical sciences, epidemiology and public health.     

Forecasting Multivariate Road Traffic Flows Using Bayesian Dynamic Graphical Models,Splines and Other Traffic Variables

Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simple model to use. While statistical flow forecasting models usually base their forecasts on flow data alone, data for other traffic variables are also routinely collected. This paper shows how cubic splines can be used to incorporate extra variables into the LMDM in order to enhance flow forecasts. Cubic splines are also introduced into the LMDM to parsimoniously accommodate the daily cycle exhibited by traffic flows. The proposed methodology allows the LMDM to provide more accurate forecasts when forecasting flows in a real high‐dimensional traffic data set. The resulting extended LMDM can deal with some important traffic modelling issues not usually considered in flow forecasting models. Additionally, the model can be implemented in a real‐time environment, a crucial requirement for traffic management systems designed to support decisions and actions to alleviate congestion and keep traffic flowing.     

Financial Well-Being Index in México

Social Indicators Research - This paper aims to construct a financial well-being index and analyze its determinants among adults in Mexico. It employs multiple correspondence analysis, multiple...     

Bimodality based on the generalized skew-normal distribution

This paper focuses on the development of a new extension of the generalized skew-normal distribution introduced in Gómez et al. [Generalized skew-normal models: properties and inference. Statistics. 2006;40(6):495–505]. To produce the generalization a new parameter is introduced, the signal of which has the flexibility of yielding unimodal as well as bimodal distributions. We study its properties, derive a stochastic representation and state some expressions that facilitate moments derivation. Maximum likelihood is implemented via the EM algorithm which is based on the stochastic representation derived. We show that the Fisher information matrix is singular and discuss ways of getting round this problem. An illustration using real data reveals that the model can capture well special data features such as bimodality and asymmetry.     

The Tanda: A Practice at the Intersection of Mathematics,Culture, and Financial Goals

We present an analysis and discussion of the tanda, a multiperson pooled credit and savings scheme (a rotating credit association or RCA), as described by two informants from Mexican immigrant communities in California. In the tanda, participants contribute regularly to a common fund which is distributed to participants on a rotating basis. We analyze the tanda at multiple levels (as a mathematical, cultural, and distributed practice) and identify points of intersection and conflict. Contrary to many formal or school-based conceptions of mathematics, mathematical work in the context of the tanda is in service of, and intimately tied up with, cultural goals and values. Likewise, cultural means and mathematics are employed to personal ends. We argue that the tanda should be of enduring interest, particularly among educators interested in bringing more authentic, culturally-relevant mathematics into classroom settings, because it so clearly illustrates how mathematical and cultural processes can interact in the context of personal goals, and provides a potentially valuable template for engaging, consequential, and successful mathematics.     

L'analyse de la variation saisonniére quand l'amplitude et la taille sont faibles

Studies of seasonal variation are valuable in biomedical research because they can help to discover the etiology of diseases that are not well understood. Generally in these studies the data have certain characteristics that require specialized tests and methods for the statistical analysis. But the effectiveness of these specialized tests is variable, especially according to the seasonal variation, the dimension of the amplitude in the seasonal variation, and the sample size. The purpose of this paper is to present a test and methods appropriate for the analysis and modeling of data whose seasonal variation has small amplitude and whose sample size is small. This test can detect different kinds of seasonal variation. The results from a simulation study show that the test performs very well. The application of these methods is illustrated by two examples.     

Bayesian analysis of seasonal variation when the sample size and the amplitude are small

We propose what appears to be the first Bayesian procedure for the analysis of seasonal variation when the sample size and the amplitude are small. Such data occur often in the medical sciences, where seasonality analyses and environmental considerations can help clarify disease etiologies. The method is explained in terms of a simple physico-geometric setting. We present the Bayesian version of a frequentist test that performs well. Two examples of real data illustrate the procedure's application.     

Improving the Measurement of the Relationship between Opportunities and Income: Evidence from Longitudinal Data from Chile

The relationship between exogenous circumstances faced in early childhood (opportunities) and earnings has been increasingly studied in the past decade. In this article we assess the sensitivity of this relationship to different income measures. Typically the empirical evidence is based on short‐term income measures that suffer from variability and measurement error. Using longitudinal data from Chile, we find that when four‐ and seven‐year earnings are used, the relationship between inequality of opportunity and income inequality is significantly higher than that obtained with yearly measures. Monte Carlo simulations with several data‐generating processes confirm this result. This supports policies targeted to reduce long‐term income inequality via providing equal opportunity to individuals at early stages in life.     

Do infrastructures influence the convergence of efficiency in Mexico?

The aim of this study is to identify disparities in technical efficiency and the influence of infrastructure on the convergence in efficiency for the states of Mexico. It has estimated a translog production function following the model by Battese & Coelli (1995). The results show that public investment has helped increase the efficient use of factors and reveal that there are opportunities to increase the pace of growth. The analysis of convergence allows us to determine that the catching up is more intensive among groups of states, and positively influenced by the infrastructure.     

Towards sustainability by aligning operational programmes and sustainable performance measures

Abstract

In the last decades, sustainable concerns have increasingly gained importance to organizational survival, and Lean/Six Sigma approaches are becoming more and more outstanding in order to improve sustainability performance. This article aims to evaluate the degree of importance of sustainable performance measures of Brazilian organizations and to propose guidelines to achieve sustainability by aligning these measures with operational improvement programmes. Multiple data collection methods were applied as theoretical literature review, questionnaire survey and semi-structured interviews with industry professionals and academic researchers. The findings show that it is the corporate responsibility to focus their efforts on both operational improvement programmes and sustainable initiatives in order to achieve better environmental protection, corporate reputation, quality management, cost performance and suppliers relations, as they are considered to be more important on organizational sustainability.