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.     

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.     

The context of employment discrimination: interpreting the findings of a field experiment

Although field experiments have documented the contemporary relevance of discrimination in employment, theories developed to explain the dynamics of differential treatment cannot account for differences across organizational and institutional contexts. In this article, I address this shortcoming by presenting the main empirical findings from a multi‐method research project, in which a field experiment of ethnic discrimination in the Norwegian labour market was complemented with forty‐two in‐depth interviews with employers who were observed in the first stage of the study. While the experimental data support earlier findings in documenting that ethnic discrimination indeed takes place, the qualitative material suggests that theorizing in the field experiment literature have been too concerned with individual and intra‐psychic explanations. Discriminatory outcomes in employment processes seems to be more dependent on contextual factors such as the number of applications received, whether requirements are specified, and the degree to which recruitment procedures are formalized. I argue that different contexts of employment provide different opportunity structures for discrimination, a finding with important theoretical and methodological implications.     

Exploring Social and Geographical Trajectories of Latin Americans in Sweden

Close to 20 percent of the Swedish population are of immigrant origin; one in eight is foreign‐born. About 45 percent of all immigrants originate from outside Europe and most of these have entered the country as refugees or relatives of refugees. Issues connected to immigration, including the number of immigrants, settlement patterns and level of social integration of ethnic minorities, have been much discussed in Sweden in recent decades. This paper focuses on the integration of Latin American immigrants in Sweden. It compares the level of integration – measured as educational achievement, labour market participation, income and housing – experienced by first and second generation migrants. I use register information allowing me to include all 1^st and 2^nd generation Latin Americans that have lived in Sweden between 1990 and 2006 (in total 127,000 individuals). Data are longitudinal, which means that individuals can be followed over time. I make use of the longitudinal material in order to study changes in residential patterns and in attempts to explain educational and employment outcomes for second generation Latin Americans. The general conclusion of the paper is that in terms of integration, LAC immigrants have an intermediate position compared to other immigrant categories; they are often better off than people from Africa and the Middle East but clearly below the level experienced by some other migrants, especially those from Western Europe. This cannot be explained by level of education. The average level of education is high for first generation immigrants from LA countries. For many people, the level of labour market participation and income increase over time but one important result of this analysis is that second generation Latin Americans seem to do less well in Sweden compared to many other second generation migrants.     

Life Experiences and Vulnerabilities of Homeless Women: A Comparison of Women Unaccompanied Versus Accompanied by Minor Children, and Correlates with Children's Emotional Distress

The histories and service needs of 2 groups of homeless women, those unaccompanied by minor children (n = 46) and those accompanied by minor children (n = 22), were compared, on the basis of their responses to a biennial survey of homeless people in a midsize city in the southeast United States. Mothers of children aged 2–10 also completed a measure of stress symptoms for their children (n = 20). Irrespective of family status group, problems such as substance abuse, history of mental illness, health problems, chronicity of homelessness, and crime victimization tended to cluster together. The findings also suggest that declining and/or inadequate levels of public benefits, Temporary Assistance to Needy Families, and Medicaid-funded health care in particular, have adversely affected the 2 groups, particularly families with children. The young children in this sample experienced elevated levels of stress, and significant associations were found between children's stress levels and certain risk factors of their mothers. Implications for service delivery are discussed.     

The identification of logistic regression models with errors in the variables

The simple logistic regression model with normal measurement error and normal regressor is shown to be identifiable without any extra information about the measurement error. The multiple logistic regression model with more than one regressor variable measured with error is not identifiable. If the covariance matrix of the measurement error is known up to a scalar factor, the model is identified. Further we discuss why in spite of the identifiability the models cannot be estimated in a reasonable way without extra information about the measurement error.