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.     

Mobile Money and School Participation: Evidence from Africa

This paper shows that mobile money technology—an electronic wallet service that allows users to deposit, transfer, and receive money using their mobile phones—is positively correlated with increased school participation of children in school age. By using data from 4 African countries, we argue that, by reducing transaction costs, and by making it easier and less expensive to receive remittances, mobile money reduces the need for coping strategies that are detrimental to child development, such as withdrawing children from school and sending them to work. We find that mobile money increases the chances of children attending school. This finding is robust to different empirical models. In a nutshell, our results show that 1 million children could start attending school in low-income countries if mobile money was available to all.

     

A reflected feature space for CART

We present an algorithm for learning oblique decision trees, called HHCART(G). Our decision tree combines learning concepts from two classification trees, HHCART and Geometric Decision Tree (GDT). HHCART(G) is a simplified HHCART algorithm that uses linear structure in the training examples, captured by a modified GDT angle bisector, to define splitting directions. At each node, we reflect the training examples with respect to the modified angle bisector to align this linear structure with the coordinate axes. Searching axis parallel splits in this reflected feature space provides an efficient and effective way of finding oblique splits in the original feature space. Our method is much simpler than HHCART because it only considers one reflected feature space for node splitting. HHCART considers multiple reflected feature spaces for node splitting making it more computationally intensive to build. Experimental results show that HHCART(G) is an effective classifier, producing compact trees with similar or better results than several other decision trees, including GDT and HHCART trees.     

Developments in gender and U.S. politics: A call for intersectionality

Since “women and politics” scholarship emerged in the 1970s, social, institutional, and theoretical developments have shaped the trajectory of U.S. scholarship in this field. First, the presence of women in formal politics has increased, albeit unevenly across parties and minority groups over time. Simultaneously, the capacity to study “political women” has become supported through institutional mechanisms such as academic journals and communities of practice. Moreover, gender as a critical focus of analysis has been developed and refined. In the literature on women and politics, the shift from studying sex differences to interrogating gendered political institutions is especially salient. This institutional focus, along with recent intersectional studies of gender and politics, increases opportunities for cross‐pollination of sociological and political science perspectives. In this review, I provide a brief history of the U.S. scholarship on gender and politics and map these relevant social, institutional, and theoretical advances. I highlight the value of recent intersectional contributions in this field and make the case for bringing partisanship—an increasingly salient political identity and structure—into intersectional approaches to gender and politics.     

Checking validity of monotone domain mean estimators

Estimates of population characteristics such as domain means are often expected to follow monotonicity assumptions. Recently, a method to adaptively pool neighbouring domains was proposed, which ensures that the resulting domain mean estimates follow monotone constraints. The method leads to asymptotically valid estimation and inference, and can lead to substantial improvements in efficiency, in comparison with unconstrained domain estimators. However, assuming incorrect shape constraints may lead to biased estimators. Here, we develop the Cone Information Criterion for Survey Data as a diagnostic method to measure monotonicity departures on population domain means. We show that the criterion leads to a consistent methodology that makes an asymptotically correct decision choosing between unconstrained and constrained domain mean estimators. The Canadian Journal of Statistics 47: 315–331; 2019 © 2019 Statistical Society of Canada     

Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.     

Deviant Peers and Adolescent Risky Behaviors: The Protective Effect of Nonverbal Display of Parental Warmth

This study examines whether nonverbal displays of parents’ warmth during an in‐lab conflict discussion mitigate the links between affiliation with deviant peers and risky behaviors. A sample of 107 youth and their parents participated in a study spanning from mid‐adolescence (T1) to late adolescence (T2). At T1, family members discussed a contentious issue, which was coded for parents’ nonverbal warmth. At T1 and T2, youth reported on their friends’ and their own risky behaviors. Fathers’ warmth moderated each prospective association between deviant peers and risky behaviors. Mothers’ warmth did not emerge as a significant moderator. Girls, in particular, benefitted from fathers’ warmth as a buffer in the trajectory from T1 risky behaviors to T2 risky behaviors and deviant peers.     

Climate regime shift detection with a trans‐dimensional,sequential Monte Carlo,variational Bayes method

We present an application study which exemplifies a cutting edge statistical approach for detecting climate regime shifts. The algorithm uses Bayesian computational techniques that make time‐efficient analysis of large volumes of climate data possible. Output includes probabilistic estimates of the number and duration of regimes, the number and probability distribution of hidden states, and the probability of a regime shift in any year of the time series. Analysis of the Pacific Decadal Oscillation (PDO) index is provided as an example. Two states are detected: one is associated with positive values of the PDO and presents lower interannual variability, while the other corresponds to negative values of the PDO and greater variability. We compare this approach with existing alternatives from the literature and highlight the potential for ours to unlock features hidden in climate data.     

The matching extension problem in general graphs is co-NP-complete

A simple connected graph G with 2n vertices is said to be k-extendable for an integer k with \(0<k<n\) if G contains a perfect matching and every matching of cardinality k in G is a subset of some perfect matching. Lakhal and Litzler (Inf Process Lett 65(1):11–16, 1998) discovered a polynomial algorithm that decides whether a bipartite graph is k-extendable. For general graphs, however, it has been an open problem whether there exists a polynomial algorithm. The new result presented in this paper is that the extendability problem is co-NP-complete.