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.     

如何促进产业集群发展?——美国亚利桑那州产业集群发展模式分析

1980年代末至1990年代初,美国亚利桑那州经济遭遇严重衰退,地区经济发展面临转型压力。1992年,亚利桑那州实施了产业集群战略,成功推动了地区经济的复苏和转型,并使其成为之后十余年美国经济增长最快的地区之一。通过对亚利桑那州产业集群战略的制定、调整和集群发展特征的深入分析发现,亚利桑那州产业集群的发展得益于科学的战略设计和以"技术密集型中小企业+企业网络组织"为双驱动的发展模式,而政府在这一过程中只扮演着"组织者"和"协调者"的角色。这对中国产业集群的发展具有重要的借鉴意义。     

储层岩石矿物成分对酸蚀裂缝导流能力的影响

储层岩石矿物成分是酸压改造效果影响因素之一。研究认为，在储层岩石矿物成分中易溶物的含量及分布方式决定了酸蚀裂缝导流能力大小，易溶蚀物的含量越多，岩石溶蚀速率越快。室内测试了不同溶蚀速率下的灰岩和白云岩的酸蚀裂缝导流能力，研究了岩石溶蚀速率、易溶蚀物含量对酸蚀裂缝导流能力的影响，对酸压工艺设计方案提出了指导性建议。研究结果表明，储层岩石溶蚀速率与酸蚀裂缝导流能力增加倍比存在抛物线关系，易溶蚀物占30%40% 时酸蚀裂缝导流能力保持较好。     

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.