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.     

Women’s mobility and ‘transport-related social exclusion’ in Bishkek

This paper analyses gendered mobilities in Bishkek in the space of the most popular form of public transport: the minibus, or ‘marshrutka’. As the means by which women often access various important sites of daily life, the marshrutka itself is a site of negotiation and interaction. Utilizing theories of mobility and empirical data, we argue that marshrutkas are spaces that can give rise to two dichotomous conditions: positive marshrutka experiences may increase the social mobility of female passengers and subsequently increase social empowerment and influence, while negative ones can provide the grounds for social exclusion and gender inequality.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

Cyclical Evolution of the Global Right

An understanding of the current right‐wing national and transnational social movements can benefit from comparing them to the global and national conditions operating during their last appearance in the first half of the twentieth century and by carefully comparing twentieth‐century fascism with the neofascist and right‐wing populist movements that have been emerging in the twenty‐first century. This allows us to assess the similarities and differences, and to gain insights about what could be the consequences of the reemergence of populist nationalism and fascist movements. Our study uses the comparative evolutionary world‐systems perspective to study the Global Right from 1800 to the present. We see fascism as a form of capitalism that emerges when the capitalist project is in crisis. World historical waves of right‐wing populism and fascism are caused by the cycles of globalization and deglobalization, the rise and fall of hegemonic core powers, long business cycles (the Kondratieff wave), and interactions with both Centrist Liberalism and the Global Left. We consider how crises of the global capitalist system have produced right‐wing backlashes in the past, and how a future terminal crisis of capitalism could lead to a reemergence of a new form of authoritarian global governance or a reorganized global democracy in the future.     

CORRUPTION: TOP DOWN OR BOTTOM UP?

This article studies the impact of corruption on an economy with a hierarchical government. In particular, we study whether centralizing corruption within the higher level of government increases or decreases the total amount of corruption. We show that when the after-tax relative profitability of the formal sector as compared to that of the informal sector is high enough, adding a layer of government increases the total amount of corruption. By contrast, for high-enough public wages and/or an efficient monitoring technology of the bureaucratic system, centralization of corruption at the top of the government hierarchy redistributes bribe income from the lower level to the upper level. In the process, total corruption is reduced and the formal sector of the economy expands.     

The Abject Embodiment of Cancer Patients: Dignity,Selfhood, and the Grotesque Body

The body is the empirical quintessence of the self. Because selfhood is symbolic, embodiment represents the personification and materialization of otherwise invisible qualities of personhood. The body and experiences of embodiment are central to our sense of being, who we think we are, and what others attribute to us. What happens, then, when one's body is humiliating? How does the self handle the implications of a gruesome body? How do people manage selfhood in light of grotesque physical appearances? This study explores these questions in the experiences of dying cancer patients and seeks to better understand relationships among body, self, and situated social interaction.     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

Consent and Informed Consent: Their Ongoing Evolutions in Clinical Care and Research on Humans

The dual concepts of 'consent' and 'informed consent' continue to have three evolutions. The primary evolution of consent in the patient–physician relationship began in Great Britain in 1767 in the British case, Slater v. Baker and Stapleton , in the judge-made law of consent. The term 'informed consent' within the patient–physician relationship entered the judicial lexicon in the 1957 California appellate case, Salgo v. Leland Stanford Junior University . In its second evolution within research on humans following the Nuremberg trials that included experimental atrocities on humans, there is a key focus on clarifying the purpose of research and specifying the reasons for the need for an even more extensive risk disclosure to individuals considering volunteering for study participation. This second evolution continues with the further refinement of the Declaration of Helsinki and, within the USA, a focus on the Belmont Report . In its third evolution in research in the social science, there has been a recognition of problems with informed consent to questionnaire research. When questionnaires involve patients with moderate or severe posttraumatic stress disorder or abused individuals, there needs to be intense consideration focused on how to best protect the participants with these conditions during the questionnaire study.