EM-Type algorithms for heavy-tailed logistic mixed models

This paper aims at evaluating different aspects of Monte Carlo expectation – maximization algorithm to estimate heavy-tailed mixed logistic regression (MLR) models. As a novelty it also proposes a multiple chain Gibbs sampler to generate of the latent variables distributions thus obtaining independent samples. In heavy-tailed MLR models, the analytical forms of the full conditional distributions for the random effects are unknown. Four different Metropolis–Hastings algorithms are assumed to generate from them. We also discuss stopping rules in order to obtain more efficient algorithms in heavy-tailed MLR models. The algorithms are compared through the analysis of simulated and Ascaris Suum data.     

Known Mean,Unknown Maxima? Testing the Maximum Knowing Only the Mean

In the quantitative group testing problem, the use of the group mean to identify if the group maximum is greater than a prefixed threshold (infected group) is analyzed, using n independent and identically distributed individuals. Under these conditions, it is shown that the information of the mean is sufficient to classify each group as infected or healthy with low probability of misclassification when the underline distribution is a unilateral heavy-tailed distribution.     

An examination of ecological predictors of health literacy in black college students

Objective: Health literacy is a determinant of health, but disparities in health literacy persist. This study examined the influence of ecological factors on college students’ health literacy. Participants: During January 2016 a nonrandom sample of black undergraduate students (n = 298) aged 18–24 were recruited from enrollment lists at two urban universities in the Southeastern United States. Methods: Information on health literacy as well as numerous intrapersonal, social, and cultural-environment factors was obtained using an electronic questionnaire and then statistically modeled. Results: Ecological factors accounted for 28.7% of the variance in health literacy. In particular, reappraisal (B = 0.323, p < .001), suppression (B = ?0.289, p < .001), campus health education (B = 0.192, p < .05), campus tobacco culture (B = ?0.174, p < .05), and perceived norms (B = ?0.153, p < .05) directly predicted health literacy. Conclusions: Although intrapersonal factors influence health literacy, the sociocultural environment of college can also foster or hinder college students’ health literacy.     

Investigating the impact of Hurricane Maria on an ongoing birth cohort in Puerto Rico

Population and Environment - Prior to Hurricane Maria, Puerto Rico already had 200+ hazardous waste sites, significant contamination of water resources, and among the highest rates of preterm birth...     

Consistent Smooth Bootstrap Kernel Intensity Estimation for Inhomogeneous Spatial Poisson Point Processes

Non‐parametric estimation and bootstrap techniques play an important role in many areas of Statistics. In the point process context, kernel intensity estimation has been limited to exploratory analysis because of its inconsistency, and some consistent alternatives have been proposed. Furthermore, most authors have considered kernel intensity estimators with scalar bandwidths, which can be very restrictive. This work focuses on a consistent kernel intensity estimator with unconstrained bandwidth matrix. We propose a smooth bootstrap for inhomogeneous spatial point processes. The consistency of the bootstrap mean integrated squared error (MISE) as an estimator of the MISE of the consistent kernel intensity estimator proves the validity of the resampling procedure. Finally, we propose a plug‐in bandwidth selection procedure based on the bootstrap MISE and compare its performance with several methods currently used through both as a simulation study and an application to the spatial pattern of wildfires registered in Galicia (Spain) during 2006.     

Multidimensional Approach for Tsunami Vulnerability Assessment: Framing the Territorial Impacts in Two Municipalities in Portugal

This study presents a new multidimensional methodology for tsunami vulnerability assessment that combines the morphological, structural, social, and tax component of vulnerability. This new approach can be distinguished from previous methodologies that focused primarily on the evaluation of potentially affected buildings and did not use tsunami numerical modeling. The methodology was applied to the Figueira da Foz and Vila do Bispo municipalities in Portugal. For each area, the potential tsunami‐inundated areas were calculated considering the 1755 Lisbon tsunami, which is the greatest disaster caused by natural hazards that ever occurred in Portugal. Furthermore, the four components of the vulnerability were calculated to obtain a composite vulnerability index. This methodology enables us to differentiate the two areas in their vulnerability, highlighting the characteristics of the territory components. This methodology can be a starting point for the creation of a local assessment framework at the municipal scale related to tsunami risk. In addition, the methodology is an important support for the different local stakeholders.     

A note on defining organisational systems for contingency theory in OM

Abstract

This paper aims to improve the applicability and relevance of contingency theory research in the field of Operations Management. Based on the results of previous studies, we have identified a systems-based single definition of organisation types that could describe the fit between organisational environment and organisational structure. This definition of organisation type, which we call an ‘organisational system’, regards the organisation as an integrated whole instead of as a sum of its parts and can help to better classify organisations in order to identify fits between organisation types and emerging practices in Operations Management.     

Eco-Six Sigma: integration of environmental variables into the Six Sigma technique

The impressive results achieved by several companies through the Six Sigma technique have motivated other organisations to follow their footsteps in search for competitiveness. As noticed in the literature, most Six Sigma projects in manufacturing have aimed to obtain process enhancements, focusing mostly on optimising product cycle times with almost none taking into consideration environmental aspects as part of their developments. Furthermore, organisations have been worrying worldwide about environmental issues, seeking to foster their eco-efficiency. As a result, the main objective of this paper is to formally introduce the environmental considerations into the Six Sigma technique by proposing a procedure to incorporate environmental variables into the DMAIC process as a way to increase the eco-efficiency level of the firms. Based on the literature review, a conceptual procedure to incorporate environmental variables into the Six Sigma DMAIC technique is proposed. To verify its possible application to real-life situations an action research was developed and could verify that the traditional Six Sigma approach (dealing only with the manufacturing process variables) was capable of improving the eco-efficiency level of the process area under study by 11%. The consideration of the environmental variables as part of the DMAIC technique as per the proposed approach allowed an additional 20% gain, which demonstrates its adequacy to increase the eco-efficiency level of the manufacturing area under evaluation.     

THE MONOPOLY OF VIOLENCE: EVIDENCE FROM COLOMBIA

Many states in Latin America, Africa, and Asia lack the monopoly of violence, even though this was identified by Max Weber as the foundation of the state, and thus the capacity to govern effectively. In this paper we develop a new perspective on the establishment of the monopoly of violence. We build a model to explain the incentive of central states to eliminate nonstate armed actors (paramilitaries) in a democracy. The model is premised on the idea that paramilitaries may choose to and can influence elections. Since paramilitaries have preferences over policies, this reduces the incentives of the politicians they favor to eliminate them. We then investigate these ideas using data from Colombia between 1991 and 2006. We first present regression and case study evidence supporting our postulate that paramilitary groups can have significant effects on elections for the legislature and the executive. Next, we show that the evidence is also broadly consistent with the implication of the model that paramilitaries tend to persist to the extent that they deliver votes to candidates for the executive whose preferences are close to theirs and that this effect is larger in areas where the presidential candidate would have otherwise not done as well. Finally, we use roll‐call votes to illustrate a possible “quid pro quo” between the executive and paramilitaries in Colombia.