Hill estimator of projections of functional data on principal components

Functional principal component scores are commonly used to reduce mathematically infinitely dimensional functional data to finite dimensional vectors. In certain applications, most notably in finance, these scores exhibit tail behaviour consistent with the assumption of regular variation. Knowledge of the index of the regular variation, α, is needed to apply methods of extreme value theory. The most commonly used method of the estimation of α is the Hill estimator. We derive conditions under which the Hill estimator computed from the sample scores is consistent for the tail index of the unobservable population scores.     

试论戏剧翻译的标准

译文的使用目的决定了不同体裁文体的翻译应遵循不同的翻译标准。本文分析了戏剧翻译的特点,提出戏剧翻译应以功能对等为原则,以原语和译语观众能获得大体相同的反应为标准,并探讨了为达到功能对等需对译文进行调整。     

系统功能语篇分析实证研究——以主位结构为例

运用Halliday系统功能语法的观点和方法,主要探讨了语篇功能赖以实现的主位系统,分析主位结构和主位推进程序在实现语篇的衔接和连贯中的作用、所具有的文体功能以及主位的选择和使用表达说话者的不同目的的关系。     

《警察与赞美诗》的功能文体学

运用功能文体学的理论,通过对三大元功能的介绍和应用对欧.亨利作品《警察与赞美诗》进行的分析。功能文体学为文学欣赏提供了有效的手段,使人们更好的对作品有了深层的理解和欣赏。     

Resampling-based Classification Using Depth for Functional Curves

The depths, which have been used to detect outliers or to extract a representative subset, can be applied to classification. We propose a resampling-based classification method based on the fact that resampling techniques yield a consistent estimator of the distribution of a statistic. The performance of this method was evaluated with eight contaminated models in terms of Correct Classification Rates (CCRs), and the results were compared with other known methods. The proposed method consistently showed higher average CCRs and 4% higher CCR at the maximum compared to other methods. In addition, this method was applied to Berkeley data. The average CCRs were between 0.79 and 0.85.     

Sparse group lasso for multiclass functional logistic regression models

Sparsity-inducing penalties are useful tools for variable selection and are also effective for regression problems where the data are functions. We consider the problem of selecting not only variables but also decision boundaries in multiclass logistic regression models for functional data, using sparse regularization. The parameters of the functional logistic regression model are estimated in the framework of the penalized likelihood method with the sparse group lasso-type penalty, and then tuning parameters for the model are selected using the model selection criterion. The effectiveness of the proposed method is investigated through simulation studies and the analysis of a gene expression data set.     

中外上市公司独立董事机制之比较——从功能定位及权责的角度

独立董事制度产生后对美国公司治理起到了很大作用，并在其他国家迅速推广发展。该制度的引入本是我国上市公司解决内部人控制、一股独大问题的有利契机，但由于各种原因，独立董事在上市公司大股东侵害中小股东利益时未能发挥其应有的效用。文章通过对比研究我国与英美国家在独立董事功能定位、职权和责任上的不同之处，对完善我国独立董事机制提出一些建议。     

功能语境视域的《论语》歧义句语内翻译模式研究

语内翻译是典籍翻译过程中一个重要步骤,其对典籍歧义句的处理直接影响最终的翻译质量。在功能语境理论框架内,采用宏观功能语境分析和自下而上的具体分析相结合的方法,以《论语·学而篇》第8章“学则不固”的“固”字为例,对《论语》歧义句语内翻译进行研究,探讨可操作的翻译模式。研究发现在歧义句语内翻译过程中进行语境还原,有助消除词汇歧义;研究认为“学则不固”的“固”字应作“固陋”解译。      

Uncertainty Analysis for Functional Computer Output: A Simulation Study in Inverse Problems

Computer models with functional output are omnipresent throughout science and engineering. Most often the computer model is treated as a black-box and information about the underlying mathematical model is not exploited in statistical analyses. Consequently, general-purpose bases such as wavelets are typically used to describe the main characteristics of the functional output. In this article we advocate for using information about the underlying mathematical model in order to choose a better basis for the functional output. To validate this choice, a simulation study is presented in the context of uncertainty analysis for a computer model from inverse Sturm-Liouville problems.     

Conditional VAR and Expected Shortfall: A New Functional Approach

We estimate two well-known risk measures, the value-at-risk (VAR) and the expected shortfall, conditionally to a functional variable (i.e., a random variable valued in some semi(pseudo)-metric space). We use nonparametric kernel estimation for constructing estimators of these quantities, under general dependence conditions. Theoretical properties are stated whereas practical aspects are illustrated on simulated data: nonlinear functional and GARCH(1,1) models. Some ideas on bandwidth selection using bootstrap are introduced. Finally, an empirical example is given through data of the S&P 500 time series.