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.     

Price bias and common practice in option pricing

Generally, the semiclosed-form option pricing formula for complex financial models depends on unobservable factors such as stochastic volatility and jump intensity. A popular practice is to use an estimate of these latent factors to compute the option price. However, in many situations this plug-and-play approximation does not yield the appropriate price. This article examines this bias and quantifies its impacts. We decompose the bias into terms that are related to the bias on the unobservable factors and to the precision of their point estimators. The approximated price is found to be highly biased when only the history of the stock price is used to recover the latent states. This bias is corrected when option prices are added to the sample used to recover the states' best estimate. We also show numerically that such a bias is propagated on calibrated parameters, leading to erroneous values. The Canadian Journal of Statistics 48: 8–35; 2020 © 2019 Statistical Society of Canada     

Relations Between Measures of Irritability and Contingency Detection at 6 Months

The hypothesis that irritability and contingency detection are negatively correlated was examined in thirty‐one 6‐month‐old infants. Observation and maternal report‐based assessments of irritability were correlated with both a criterion score and a continuous score of contingency detection. Results indicated that greater irritability in infants was associated with lower contingency detection scores. Discussion focuses on identifying processes by which the 2 constructs may be associated.     

Quantifying uncertainty in the biospheric carbon flux for England and Wales

Summary.  A crucial issue in the current global warming debate is the effect of vegetation and soils on carbon dioxide (CO₂) concentrations in the atmosphere. Vegetation can extract CO₂ through photosynthesis, but respiration, decay of soil organic matter and disturbance effects such as fire return it to the atmosphere. The balance of these processes is the net carbon flux. To estimate the biospheric carbon flux for England and Wales, we address the statistical problem of inference for the sum of multiple outputs from a complex deterministic computer code whose input parameters are uncertain. The code is a process model which simulates the carbon dynamics of vegetation and soils, including the amount of carbon that is stored as a result of photosynthesis and the amount that is returned to the atmosphere through respiration. The aggregation of outputs corresponding to multiple sites and types of vegetation in a region gives an estimate of the total carbon flux for that region over a period of time. Expert prior opinions are elicited for marginal uncertainty about the relevant input parameters and for correlations of inputs between sites. A Gaussian process model is used to build emulators of the multiple code outputs and Bayesian uncertainty analysis is then used to propagate uncertainty in the input parameters through to uncertainty on the aggregated output. Numerical results are presented for England and Wales in the year 2000. It is estimated that vegetation and soils in England and Wales constituted a net sink of 7.55 Mt C (1 Mt C = 10¹² g of carbon) in 2000, with standard deviation 0.56 Mt C resulting from the sources of uncertainty that are considered.     

The Chinese-speaking Muslims (Dungans) of Central Asia: A Case of Multiple Identities in a Changing Context

Chinese-speaking Muslims, called Dungans in Central Asia and Huizu in China, were forced to migrate to present-day Kazakhstan, Kyrgyzstan and Uzbekistan at the end of the nineteenth century. They reconstructed their identity through interrelations with local populations and the soviet categorisation of nationality. But after 1991, their status has being gradually destabilised owing to the changes in economic, social and political rules as well as citizenship regulations in these countries. They have to readapt to the new situation created by the independence of the three former Soviet Republics, which has challenged their unity.     

Book Review: Strategic Management for Voluntary Nonprofit Organizations,by Roger Courtney. Routledge,New York, 2002

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations -     

Analyse biographique du travail féminin

Cet article montre comment l'application d'une méthode d'analyse biographique d'interaction à un domaine où l'interaction paraît évidente — activité féminine et fécondite — ouvre des perspectives nouvelles. En effet, les limites qui apparaissent lors de cette analyse conduisent les auteurs à conclure que ce n'est pas en raffinant les méthodes mais plutôt la collecte que l'on parviendra à progresser dans ce domaine. Le problème des interactions de l'activité et de la fécondité semble mal posé avant même le recours à la méthode. Cela justifie la nécessité de saisir le travail féminin comme concept unifié ne dissociant pas la sphère domestique et la sphère rémunérée. Cela conduit les auteurs à dégager quelques principes de collecte mieux adaptés aux préoccupations récentes dans ce domaine: des données biographiques sur le travail féminin. L'interaction collectée à sa source se prêterait alors à une analyse biographique plus révélatrice que celle qui confronte deux processus sans en évaluer les modalités changeantes.This article demonstrates how life event history analysis applied to the interaction between fertility and female economic activity can lead to new perspectives in the analysis of this complicated relationship. The authors identify limits to the analysis of the interaction given the anticipatory nature of behaviour. They conclude that progress in this field requires not the refinement of methods but rather a fundamental change in the approach to the data collected. If fertility and employment are considered separately, a problem is created even before the methods of analysis are applied. They recommend a conception of women's work as a whole, a conception that does not dissociate domestic and paid work. The authors go on to identify ways of improving the relevance of data collection to contemporary issues in the life history analysis of women's work. They argue that data on the interaction, collected at its source, would yield a more revealing life history than that which sees domestic work and paid work as separate spheres, and fails to take account of changes in their relationship.     

Bayesian analysis of elapsed times in continuous‐time Markov chains

The authors consider Bayesian analysis for continuous‐time Markov chain models based on a conditional reference prior. For such models, inference of the elapsed time between chain observations depends heavily on the rate of decay of the prior as the elapsed time increases. Moreover, improper priors on the elapsed time may lead to improper posterior distributions. In addition, an infinitesimal rate matrix also characterizes this class of models. Experts often have good prior knowledge about the parameters of this matrix. The authors show that the use of a proper prior for the rate matrix parameters together with the conditional reference prior for the elapsed time yields a proper posterior distribution. The authors also demonstrate that, when compared to analyses based on priors previously proposed in the literature, a Bayesian analysis on the elapsed time based on the conditional reference prior possesses better frequentist properties. The type of prior thus represents a better default prior choice for estimation software.