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.     

椭圆轮廓的评定方法及其计算机模拟

给出了一个正确的数据处理方法,对已有的机械零件的椭圆轮廓的几何参数和线轮廓度误差评定理论的不足作了修正,并用计算机模拟方法对其进行了验证。     

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.     

Local estimation for varying-coefficient models with longitudinal data

Varying-coefficient models are very useful for longitudinal data analysis. In this paper, we focus on varying-coefficient models for longitudinal data. We develop a new estimation procedure using Cholesky decomposition and profile least squares techniques. Asymptotic normality for the proposed estimators of varying-coefficient functions has been established. Monte Carlo simulation studies show excellent finite-sample performance. We illustrate our methods with a real data example.     

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。     

Identifying Latent Structures in Panel Data

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier‐Lasso (C‐Lasso) that serves to shrink individual coefficients to the unknown group‐specific coefficients. C‐Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C‐Lasso also achieves the oracle property so that group‐specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C‐Lasso is preserved in some special cases. Simulations demonstrate good finite‐sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.