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.     

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.     

英文影片的中国方言版字幕翻译

2012年3D 版《泰坦尼克号》的华丽上映曾掀起影迷们对它的新一轮热捧。文章就同样备受中国观众喜爱的中国方言版《泰坦尼克号》为案例，结合功能目的论来探寻中国方言版字幕翻译的目的与性质，进而分析、总结英文影片字幕的中国方言版译制活动过程。     

Fast and approximate exhaustive variable selection for generalised linear models with APES

We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps‐and‐bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super‐model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best‐subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.     

高频数据瞬时波动率核估计的窗宽选择及算法研究

在瞬时波动率的各种估计量中，非参数估计量因其能准确地度量瞬时波动率，一直是学者们的研究热点。然而，这类估计量在实际应用中都面临着最优窗宽的确定问题。由于最优窗宽中往往携带一些难以估计的未知参数，使得在实际应用过程中确定最优窗宽的具体数值存在困难。本文以瞬时波动率的核估计量为例，借鉴非参数回归分析中窗宽选择的思想，构建了一种能从数据中准确计算出最优窗宽具体值的算法。理论的分析和数值上的验证表明：文中所构建的算法具有良好的稳定性、适应性和收敛速度。算法的提出为瞬时波动率的后续应用研究铺平道路。     

基于股价预测的泛证券投资组合策略

本文基于期望效用最大化和L₁-中位数估计研究了在线投资组合选择问题。与EG（Exponential Gradient）策略仅利用单期价格信息估计价格趋势不同，本文将利用多期价格信息估计价格趋势，以提高在线策略的性能。首先，基于多期价格数据，利用L₁-中位数估计得到预期价格趋势。然后，通过期望效用最大化，提出一个新的具有线型时间复杂度的在线策略，EGLM（Exponential Gradient via L₁-Median）。并通过相对熵函数定义资产权重向量的距离，进而证明了EGLM策略具有泛证券投资组合性质。最后，利用国内外6个证券市场的历史数据进行实证分析，结果表明相较于UP（Universal Portfolio）策略和EG策略，EGLM策略有更好的竞争性能。     

异质品双渠道供应链下制造商的渠道决策研究

研究由两个制造商、两个零售商以及消费者组成的双渠道供应链，制造商的最优产品分销渠道策略问题，通过构建Stackelberg主从动态博弈模型，分析制造商在不同渠道选择下的产品质量水平及定价决策，消费者三种不同渠道偏好下渠道竞争和品牌竞争激烈程度对制造商渠道选择均衡结果及帕累托最优选择的影响，以及对产品质量水平、产品价格、制造商及零售商利润的影响.研究表明：品牌和渠道双重竞争下，两种竞争之间存在相互作用的关系，并共同影响制造商最终渠道选择，制造商通过权衡价格决策的后动优势与消费者渠道偏好对其利润的贡献选择是否开辟双渠道，产品价格与产品质量水平正相关，与产品的替代率负相关，产品质量水平对渠道价格的影响程度等于消费者对该渠道的偏好程度.当消费者偏好某一渠道时，制造商可在该渠道上的产品采取优质高价策略，但渠道上产品性价比降低，当品牌竞争越激烈，产品价格越低，对制造商和零售商的利润挤压越严重.     

Using local linear kernel smoothers to test the lack of fit of nonlinear regression models

Herein, we propose a data-driven test that assesses the lack of fit of nonlinear regression models. The comparison of local linear kernel and parametric fits is the basis of this test, and specific boundary-corrected kernels are not needed at the boundary when local linear fitting is used. Under the parametric null model, the asymptotically optimal bandwidth can be used for bandwidth selection. This selection method leads to the data-driven test that has a limiting normal distribution under the null hypothesis and is consistent against any fixed alternative. The finite-sample property of the proposed data-driven test is illustrated, and the power of the test is compared with that of some existing tests via simulation studies. We illustrate the practicality of the proposed test by using two data sets.     

基于定货合同的供应商柔性分析和选择模型

供应链及供应链管理是目前国内外学术界和工商企业管理者共同关心的课题,他们提出了众多分析方法和分析模型,但由于计算复杂,在实际操作中运用较少。实际上,在供应链业务流程中,供应合同是最关键的法律文件,合同信息分析能很好地减少不确定性,降低风险。因此,供应商和购货商选择模型应包括以下参数:单位供应价格、定货周期、最小定货提前期、每个周期最小定货量、临时配送补偿系数、定货量小于最小定货量补偿系数。     

论邓小平对选拔培养中青年干部的思想

建设有中国特色的社会主义市场经济 ,需要一大批杰出的人才。如何选拔培养中青年干部成为我们党和国家一项重大课题。运用邓小平理论中关于选拔培养中青年干部的观点并指导实践 ,将会对我国的四化建设起到积极作用