Model selection criteria in beta regression with varying dispersion

We address the issue of model selection in beta regressions with varying dispersion. The model consists of two submodels, namely: for the mean and for the dispersion. Our focus is on the selection of the covariates for each submodel. Our Monte Carlo evidence reveals that the joint selection of covariates for the two submodels is not accurate in finite samples. We introduce two new model selection criteria that explicitly account for varying dispersion and propose a fast two step model selection scheme which is considerably more accurate and is computationally less costly than usual joint model selection. Monte Carlo evidence is presented and discussed. We also present the results of an empirical application.     

Thermal comfort and personal protective equipment (PPE)

Personal Protective Equipment (PPE) is considered the last preferred solution to an Occupational Safety Health (OSH) system. Brazilian OSH legislation assumes and requires PPE usage to neutralize the possibility of pesticide contamination. This study examines the inadequacy of the PPE used in a tomato crop, particularly from the standpoint of thermal comfort. This study made observations regarding heart rate and body temperature in a controlled environment using a treadmill (with and without PPE) of 12 volunteers; and during the process of rural labor (at rest and in normal work routine) of 2 actual rural workers. Comparing the results of the treadmill test (with and without PPE) which tried to reproduce the same level of effort and environmental conditions of the actual rural work, this study demonstrated that PPE makes thermoregulation harder and tympanic temperature reaches higher values with the same level of effort. Moreover, body temperature increases quickly: with PPE it took 15 minutes to raise 1°C in body temperature, compared to 40 minutes without PPE. The results of this study indicated that the use inadequate PPE might pose thermoregulation risk to rural workers.     

Who Settles for Less? Subjective Dispositions,Objective Circumstances,and Housing Satisfaction

In recent years there has been growing interest in individuals’ self-perceptions of their wellbeing on the grounds that these complement well-established objective indicators of welfare. However, individuals’ assessments depend on both objective circumstances and subjective, idiosyncratic dispositions, such as aspirations and expectations. We add to the literature by formulating a modelling strategy that uncovers how these subjective dispositions differ across socio-demographic groups. This is then tested using housing satisfaction data from a large-scale household panel survey from Australia. We find that there are significant differences in the way in which individuals with different characteristics rate the same objective reality. For instance, male, older, migrant, and Indigenous individuals rate equal housing conditions more favourably than female, younger, Australian-born, and non-Indigenous individuals. These findings have important implications for how self-reported housing satisfaction, and wellbeing data in general, are to be used to inform evidence-based policy.     

The bivariate alpha-skew-normal distribution

In this paper, we propose a new bivariate distribution, namely bivariate alpha-skew-normal distribution. The proposed distribution is very flexible and capable of generalizing the univariate alpha-skew-normal distribution as its marginal component distributions; it features a probability density function with up to two modes and has the bivariate normal distribution as a special case. The joint moment generating function as well as the main moments are provided. Inference is based on a usual maximum-likelihood estimation approach. The asymptotic properties of the maximum-likelihood estimates are verified in light of a simulation study. The usefulness of the new model is illustrated in a real benchmark data.     

Graphical and Numerical Methods for Detecting Influential Observations in Complex Bingham Data

We describe methods to detect influential observations in a sample of pre-shapes when the underlying distribution is assumed to be complex Bingham. One of these methods is based on Cook's distance, which is derived from the likelihood of the complex Bingham distribution. Other method is related to the tangent space, which is based on the local influence for the multivariate normal distribution. A method to detect outliers is also explained. The application of the methods is illustrated in both a real dataset and a simulated sample.     

Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.     

Assessing the relevance of higher education courses

The establishment of the European Higher Education Area has involved specifying lists of professional competencies that programs are expected to develop, and with this the need for procedures to measure how every course within a higher education program is aligned with the program's competencies. We propose an instrument for characterizing this alignment, a process that we call assessing the relevance of a course. Using information from the course syllabus (objectives, contents and assessment scheme), our instrument produces indicators for characterizing the syllabus in terms of a competence list and for assessing its coherence. Because assessment involves quality, the results obtained can also be used to revise and improve the course syllabus. We illustrate this process with an example of a methods course from a mathematics teacher education program at a Spanish university.     

Initiative in work teams: adaptation and validation of the Personal Initiative at Group Level Scale / Iniciativa en los equipos de trabajo: adaptación y validación de la Escala de Iniciativa Personal a nivel Grupal

In this study, we conceptualize personal initiative as a collective construct and adapt and validate a scale to evaluate it with a sample of 308 Spanish participants belonging to 91 work teams. Personal initiative at group level is a behavioural syndrome in which the team shows an approach to work that is self-initiated, proactive, persistent, capable of modifying the atmosphere and pro organization. As a predictive variable, we analysed the climate for initiative as well as personal initiative at group level. The resulting variables that were analysed referred to organization and team performance, with the team productivity and innovation as the criteria, analysing radical innovation. The scale has suitable psychometric properties. The results show that there is a relationship between the two predictive variables. Furthermore, personal initiative at group level is related to team productivity, while the climate for initiative is related to innovation.