Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Abusive Supervision and Well-Being of Filipino Migrant Workers in Macau: Consequences for Self-Esteem and Heritage Culture Detachment

Abusive supervision in the workplace has been shown to have important direct consequence in work and work relationship, and also indirect consequences to workers’ well-being and relationships outside work. Consequences of abusive supervision have not been studied among migrant workers whose status in the host country of work is dependent on maintaining the work contract. This study investigates abusive supervision in 247 Filipino migrant workers in Macau, who hold temporary work contracts and work visas to engage in various low-skilled work (e.g., domestic helper, security guard, etc.). The study tests a model representing the indirect consequences of abusive supervision on the self-esteem and acculturation orientation of migrant workers, in particular, on the tendency to reject their heritage culture in their attempt to acculturate in the host country. Mediation analysis indicated that abusive supervisory perceptions led to lower self-esteem (b = ?.19), which in turn relates to tendency to reject their heritage culture as part of acculturation (b = ?.45) [indirect effect = .08, 90 % CI .04, .15]. The rejection of heritage culture is interpreted as a coping response to the negative indirect consequences of abusive supervision perceptions that may be partly attributed to being a migrant Filipino worker. The results are discussed in terms of how the acculturation of migrant workers reflects aspects of their well-being that may be adversely affected by vocational-related stress in the host country.     

On the <Emphasis Type="Italic">m</Emphasis>-clique free interval subgraphs polytope: polyhedral analysis and applications

In this paper we study the m-clique free interval subgraphs. We investigate the facial structure of the polytope defined as the convex hull of the incidence vectors associated with these subgraphs. We also present some facet-defining inequalities to strengthen the associated linear relaxation. As an application, the generalized open-shop problem with disjunctive constraints (GOSDC) is considered. Indeed, by a projection on a set of variables, the m-clique free interval subgraphs represent the solution of an integer linear program solving the GOSDC presented in this paper. Moreover, we propose exact and heuristic separation algorithms, which are exploited into a Branch-and-cut algorithm for solving the GOSDC. Finally, we present and discuss some computational results.     

Vocational Experiences and Quality of Life of Migrants: Overview and Future Research

Quality of life is a complicated concept that has attracted broad attention from multiple disciplines. This article introduces a set of studies that employ diverse approaches to explore how work, vocational, and career experiences shape quality of life among migrants, and proposes a future agenda to extend this stream of research. Specifically, operationalizing quality of life in different ways, research showcased in this special issue has demonstrated how international and intranational migrants achieve, maintain, and enhance their well-being in different life domains. Reflecting on the literature and the showcased studies, this article proposes that future research should advance the current inquiry by better categorizing vocational experiences, uniquely operationalizing of quality of life in migration contexts, taking a more culturally sensitive perspective, and broadening methodological approaches.     

If My Blood Pressure Is High,Do I Take It to Heart? Behavioral Effects of Biomarker Collection in the Health and Retirement Study

Starting in 2006, respondents in the biennial U.S. Health and Retirement Study were asked to submit biomarkers every other wave and were notified of several results. Rates of undiagnosed high blood pressure and diabetes according to these biomarkers were 1.5 % and 0.7 %, respectively. An intent-to-treat analysis suggests that collection and notification had small effects on the average respondent and may have reduced health care utilization. Among respondents who received notification of potentially dangerous biomarker levels, subsequent rates of new diagnosis and associated pharmaceutical usage increased by 20 to 40 percentage points, an order of magnitude above baseline. High blood glucose A1C was associated with a 2.2 % drop in weight and an increase in exercise among respondents without a previous diagnosis of diabetes. Notifications appear also to have altered health behaviors by spouses, suggesting household responses to health maintenance. Biomarker collection seems to have altered circumstances for an interesting minority of HRS respondents.     

Household Assets,the Role of Government Assistance,and Depression Among Low-Income Families in Shanghai

As China’s economy is rapidly changing from a planned to a capitalist economy, many families find themselves financially struggling. In some cases, conflicting values and attitudes may contribute to mental health challenges such as depression that would lead to further feelings of helplessness and immobilization. Using a random sample of 1006 low-income households from Pudong District of Shanghai, China, this study aims to examine the relationships between household assets, beliefs about government as the primary way to improve economic circumstances and self-reported depressive symptoms. In addition, this study investigates the mediation effects of beliefs that government is the best change agent for improved life circumstances on the relationship between household assets and depression. We found those who indicated that government was the main means for attaining a better life had significantly higher depression levels whereas higher numbers of household assets were associated with lower depression levels. We also found that viewing government as the most important change agent only partially mediated the relationship between household assets and depression (p?<?.001). Findings from this study support anti-poverty policies and social work related practice initiatives aimed at assisting low income families in China, in particular the need to address psychological as well as economic needs.

     

‘Rachel's not here’: Constructed dialogue in gossip

Sociolinguistic research has demonstrated that gossip is a co‐constructed phenomenon, which allows participants to establish solidarity and build alignments with each other, while evaluating an absent party. Gossip can also serve important social functions, such as helping to establish and reconfirm group norms and values. The present study provides a detailed analysis of an extended gossip episode that occurred within an institutional context: a study group interaction at a U.S. university. Our analysis shows how, in gossip, constructed dialogue both prompts and legitimizes pejorative evaluations towards an absent third party, and is actually the pivot around which group members negotiate values and norms in the process of arriving at a shared moral stance. Our analysis also demonstrates that alignment in gossip interactions is tenuous and must be continuously renewed.     

A new generalized Weibull distribution in income economic inequality curves

Researchers have been developing various extensions and modified forms of the Weibull distribution to enhance its capability for modeling and fitting different data sets. In this note, we investigate the potential usefulness of the new modification to the standard Weibull distribution called odd Weibull distribution in income economic inequality studies. Some mathematical and statistical properties of this model are proposed. We obtain explicit expressions for the first incomplete moment, quantile function, Lorenz and Zenga curves and related inequality indices. In addition to the well-known stochastic order based on Lorenz curve, the stochastic order based on Zenga curve is considered. Since the new generalized Weibull distribution seems to be suitable to model wealth, financial, actuarial and especially income distributions, these findings are fundamental in the understanding of how parameter values are related to inequality. Also, the estimation of parameters by maximum likelihood and moment methods is discussed. Finally, this distribution has been fitted to United States and Austrian income data sets and has been found to fit remarkably well in compare with the other widely used income models.     

On Bartlett correction of empirical likelihood for regularly spaced spatial data

Bartlett correction constitutes one of the attractive features of empirical likelihood because it enables the construction of confidence regions for parameters with improved coverage probabilities. We study the Bartlett correction of spatial frequency domain empirical likelihood (SFDEL) based on general spectral estimating functions for regularly spaced spatial data. This general formulation can be applied to testing and estimation problems in spatial analysis, for example testing covariance isotropy, testing covariance separability as well as estimating the parameters of spatial covariance models. We show that the SFDEL is Bartlett correctable. In particular, the improvement in coverage accuracies of the Bartlett‐corrected confidence regions depends on the underlying spatial structures. The Canadian Journal of Statistics 47: 455–472; 2019 © 2019 Statistical Society of Canada