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排序方式: 共有2546条查询结果,搜索用时 234 毫秒
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
2.
When a candidate predictive marker is available, but evidence on its predictive ability is not sufficiently reliable, all‐comers trials with marker stratification are frequently conducted. We propose a framework for planning and evaluating prospective testing strategies in confirmatory, phase III marker‐stratified clinical trials based on a natural assumption on heterogeneity of treatment effects across marker‐defined subpopulations, where weak rather than strong control is permitted for multiple population tests. For phase III marker‐stratified trials, it is expected that treatment efficacy is established in a particular patient population, possibly in a marker‐defined subpopulation, and that the marker accuracy is assessed when the marker is used to restrict the indication or labelling of the treatment to a marker‐based subpopulation, ie, assessment of the clinical validity of the marker. In this paper, we develop statistical testing strategies based on criteria that are explicitly designated to the marker assessment, including those examining treatment effects in marker‐negative patients. As existing and developed statistical testing strategies can assert treatment efficacy for either the overall patient population or the marker‐positive subpopulation, we also develop criteria for evaluating the operating characteristics of the statistical testing strategies based on the probabilities of asserting treatment efficacy across marker subpopulations. Numerical evaluations to compare the statistical testing strategies based on the developed criteria are provided. 相似文献
3.
针对我国政府、企业和银行等金融机构共同关注的债转股问题,基于债务协商谈判思想,建立部分债务股权互换模型,计算公司证券价格,探讨了债转股对公司价值、破产概率、破产损失成本和资本结构的影响,给出了银行等债权人愿意债转股的充分条件。结果表明:在事先破产清算协议贷款下,事后全部债转股总能提高公司股权价值,但并不一定能提高债券价值。只有其协商谈判能力满足一定条件,公司债权人才愿意事后选择债转股,实现帕累托改进、提高社会福利水平。其次,在公司股东协商谈判能力的一定范围内,部分债转股能提高公司价值,其最优转股债息比例随着公司资产风险的增大而增加。再次,债转股能降低公司破产风险和破产损失成本,但同时也提高了债券风险溢价。最后,随着股东谈判能力增强,最优协商转股债务比例、杠杆率都减少,而债券风险溢价增大。本文所得结果对我国政府、企业和银行如何实施债转股提供理论参考和实践指导。 相似文献
4.
A. M. Abd El-Raheem 《Journal of Statistical Computation and Simulation》2019,89(16):3075-3104
The generalized half-normal (GHN) distribution and progressive type-II censoring are considered in this article for studying some statistical inferences of constant-stress accelerated life testing. The EM algorithm is considered to calculate the maximum likelihood estimates. Fisher information matrix is formed depending on the missing information law and it is utilized for structuring the asymptomatic confidence intervals. Further, interval estimation is discussed through bootstrap intervals. The Tierney and Kadane method, importance sampling procedure and Metropolis-Hastings algorithm are utilized to compute Bayesian estimates. Furthermore, predictive estimates for censored data and the related prediction intervals are obtained. We consider three optimality criteria to find out the optimal stress level. A real data set is used to illustrate the importance of GHN distribution as an alternative lifetime model for well-known distributions. Finally, a simulation study is provided with discussion. 相似文献
5.
We study the asymptotic behavior of the marginal expected shortfall when the two random variables are asymptotic independent but positively associated, which is modeled by the so-called tail dependent coefficient. We construct an estimator of the marginal expected shortfall, which is shown to be asymptotically normal. The finite sample performance of the estimator is investigated in a small simulation study. The method is also applied to estimate the expected amount of rainfall at a weather station given that there is a once every 100 years rainfall at another weather station nearby. 相似文献
6.
针对家用电冰箱开、停机过程产生的能量损失,提出了一种节能阀的设想,并采用模拟节能阀系统加以验证,实验结果表明了它的可行性和节能效果。 相似文献
7.
Computing maximum likelihood estimates from type II doubly censored exponential data 总被引:1,自引:0,他引:1
Arturo J. fernández José I. Bravo Íñigo De Fuentes 《Statistical Methods and Applications》2002,11(2):187-200
It is well-known that, under Type II double censoring, the maximum likelihood (ML) estimators of the location and scale parameters, θ and δ, of a twoparameter exponential distribution are linear functions
of the order statistics. In contrast, when θ is known, theML estimator of δ does not admit a closed form expression. It is shown, however, that theML estimator of the scale parameter exists and is unique. Moreover, it has good large-sample properties. In addition, sharp
lower and upper bounds for this estimator are provided, which can serve as starting points for iterative interpolation methods
such as regula falsi. Explicit expressions for the expected Fisher information and Cramér-Rao lower bound are also derived.
In the Bayesian context, assuming an inverted gamma prior on δ, the uniqueness, boundedness and asymptotics of the highest
posterior density estimator of δ can be deduced in a similar way. Finally, an illustrative example is included. 相似文献
8.
讨论增长曲线模型Y =X1BX2 +ε中回归矩阵B的函数C1BC2 的估计L1YL2 +A ,在矩阵损失 (LT2 L1)Y +A - (ST2 XT2 S1X1)B (LT2 L1)Y +A - (ST2 XT2 S1X1)B T 下 ,我们得到了非齐次线性估计L1YL2 +A在非齐次线性估计类Г ={L1YL2 +A|L1:t×p ,L2 ;n×n ,A :t×s均为已知实阵 }中可容许的充要条件 :L1YL2在Г0 ={L1YL2 |L1:t×p ,L2 :n×s均为已知实阵 }中容许且当LT2 XT2 L1X1=ST2 XT2 S1X1时有A =0。 相似文献
9.
On Optimality of Bayesian Wavelet Estimators 总被引:2,自引:0,他引:2
Felix Abramovich Umberto Amato Claudia Angelini 《Scandinavian Journal of Statistics》2004,31(2):217-234
Abstract. We investigate the asymptotic optimality of several Bayesian wavelet estimators, namely, posterior mean, posterior median and Bayes Factor, where the prior imposed on wavelet coefficients is a mixture of a mass function at zero and a Gaussian density. We show that in terms of the mean squared error, for the properly chosen hyperparameters of the prior, all the three resulting Bayesian wavelet estimators achieve optimal minimax rates within any prescribed Besov space for p ≥ 2. For 1 ≤ p < 2, the Bayes Factor is still optimal for (2 s +2)/(2 s +1) ≤ p < 2 and always outperforms the posterior mean and the posterior median that can achieve only the best possible rates for linear estimators in this case. 相似文献
10.
BAYESIAN SUBSET SELECTION AND MODEL AVERAGING USING A CENTRED AND DISPERSED PRIOR FOR THE ERROR VARIANCE 总被引:1,自引:0,他引:1
Edward Cripps Robert Kohn David Nott 《Australian & New Zealand Journal of Statistics》2006,48(2):237-252
This article proposes a new data‐based prior distribution for the error variance in a Gaussian linear regression model, when the model is used for Bayesian variable selection and model averaging. For a given subset of variables in the model, this prior has a mode that is an unbiased estimator of the error variance but is suitably dispersed to make it uninformative relative to the marginal likelihood. The advantage of this empirical Bayes prior for the error variance is that it is centred and dispersed sensibly and avoids the arbitrary specification of hyperparameters. The performance of the new prior is compared to that of a prior proposed previously in the literature using several simulated examples and two loss functions. For each example our paper also reports results for the model that orthogonalizes the predictor variables before performing subset selection. A real example is also investigated. The empirical results suggest that for both the simulated and real data, the performance of the estimators based on the prior proposed in our article compares favourably with that of a prior used previously in the literature. 相似文献