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1.
Dennis Wagenaar Tiaravanni Hermawan Marc J. C. van den Homberg Jeroen C. J. H. Aerts Heidi Kreibich Hans de Moel Laurens M. Bouwer 《Risk analysis》2021,41(1):37-55
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. 相似文献
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Mark A. van de Wiel Dennis E. Te Beest Magnus M. Münch 《Scandinavian Journal of Statistics》2019,46(1):2-25
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. 相似文献
6.
Cathy W. S. Chen Hong Than-Thi Mike K. P. So 《Journal of Statistical Computation and Simulation》2019,89(2):191-210
This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei. 相似文献
7.
Carlos J. Pérez-González Arturo J. Fernández 《Journal of Statistical Computation and Simulation》2019,89(13):2489-2504
An accurate procedure is proposed to calculate approximate moments of progressive order statistics in the context of statistical inference for lifetime models. The study analyses the performance of power series expansion to approximate the moments for location and scale distributions with high precision and smaller deviations with respect to the exact values. A comparative analysis between exact and approximate methods is shown using some tables and figures. The different approximations are applied in two situations. First, we consider the problem of computing the large sample variance–covariance matrix of maximum likelihood estimators. We also use the approximations to obtain progressively censored sampling plans for log-normal distributed data. These problems illustrate that the presented procedure is highly useful to compute the moments with precision for numerous censoring patterns and, in many cases, is the only valid method because the exact calculation may not be applicable. 相似文献
8.
de Jong Petra W. Caarls Kim de Valk Helga A. G. 《Population research and policy review》2022,41(2):671-694
Population Research and Policy Review - The welfare state can be perceived as a safety net which helps individuals adjust to situations of risk or transition. Starting from this idea of the welfare... 相似文献
9.
This study investigated the risk factors associated with the occurrence of child physical and psychological abuse in South Korea based on the ecological theory of child maltreatment. A subsample of 3‐ to 18‐year‐old children from “A Study on the Current State of Child Abuse and Neglect,” a nationally representative study on child abuse and neglect, was utilized for secondary data analysis. The sample was divided into two age groups (third graders in elementary school and below and fourth graders and above). We utilized hierarchical logistic regression for each age group separately to analyze the data. The variables at each level of the system (i.e., ontogenic development, microsystem, and exosystem) were entered into the model in sequential order. The results showed the common risk factors for both age groups are the caregiver's experience of abuse in childhood, the child's problem behavior, exposure to domestic violence, community size, and informal social control. The child's age was also significant in both groups but in the opposite direction. Social support was a significant predictor for the younger age group only, while the caregiver's level of education was significant for the older age group only. Implications for future research and practice are discussed based on the study results. 相似文献
10.
Sant’Anna Annibal Parracho de Freitas Siqueira Sadok Menna Barreto Márcia 《Social indicators research》2020,148(3):733-746
Social Indicators Research - This paper analyses the Human Development Index (HDI) time series from 2010 to 2017. An alternative index is studied, which combines the same components of the HDI by... 相似文献