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.     

Catering to sex,sexual, and gender diversity: An exploratory study on the effects of LGBTI awareness training on aged care staff in Tasmania,Australia

The Australian National LGBTI Ageing and Aged Care Strategy seeks to address the lack of recognition of older lesbian, gay, bisexual, transgender, and intersex (LGBTI) adults living in aged care. Awareness training for aged care staff forms a crucial part of this strategy, but the impact of such educational programs are yet to be examined. In this article, we examine the effects of LGBTI awareness training on staff working in an Australian aged care facility. These staff perceived the awareness training as valuable, but the outcomes from it were limited. Therefore, we indicate changes that are necessary to support the translation of training into practice.     

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.     

Changing Frameworks in Attitudes Toward Abortion

For more than two decades, legal abortion has been the subject of heated political debate and adversarial social movement activity; however, national polls have shown little change in aggregate levels of support for abortion. This analysis examines how the determinants of abortion attitudes have changed between 1977 and 1996, using data from the General Social Surveys. While in early time periods, whites were more approving of abortion than blacks, that pattern had reversed by the late 1980s. After controlling for other factors, older people are more accepting of abortion throughout the two decades, while gender is generally unrelated to abortion views. Catholic religion weakens slightly as a predictor of abortion attitudes, while religious fundamentalism and political liberalism increase in explanatory power. The associations between attitudinal correlates and abortion approval also change over this time period. Religiosity becomes a less powerful predictor of abortion attitudes, while respondents' attitude toward sexual freedom and belief in the sanctity of human life increase in their predictive power. Support for gender inequality remains a weak but stable predictor of abortion attitudes. This pattern of results suggests that the public is influenced more by the pro-life framework of viewing abortion than by the pro-choice perspective.     

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.