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.     

Feminist and empowerment theory and social work practice

Feminist and empowerment theories are especially important to the understanding of individual and sociopolitical levels of social work assessment and intervention. Incorporating feminist and empowerment approaches in practice will provide social workers with the knowledge, values and skills most likely to promote human rights and social justice. In this paper, we present an overview of both theories and illustrate them with a case example.     

A new long-term survival model with dispersion induced by discrete frailty

Lifetime Data Analysis - Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there...     

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.     

Behavioral responses of rural and urban greater white-toothed shrews (Crocidura russula) to sound disturbance

Urban Ecosystems - The development of urban areas imposes challenges that wildlife must adapt to in order to persist in these new habitats. One of the greatest changes brought by urbanization has...     

The Welfare State as Safety Net in Migration Preferences: Empirical Evidence from an Experiment Among Dutch Master Students

Population Research and Policy Review - The welfare state can be perceived as a safety net which helps individuals adjust to situations of risk or transition. Starting from this idea of the welfare...