读朱新天著《印度教万神殿艺术——印度王后井探秘》

朱新天博士的新著《印度教万神殿艺术——印度王后井探秘》,是她近20年来苦心研究印度艺术的结晶。该书已分别以中、英、法三种文字同时出版,在国际印度艺术研究领域产生了重要影响。     

金融危机下中国创业投资“囚徒困境”演化研究

创业投资家和创业企业家的＂囚徒困境＂是制约中国创业投资进一步发展的主要障碍之一。对短期利益的追求和缺乏信任是造成中国创业投资＂囚徒困境＂的两个重要原因。在金融危机背景下,＂囚徒困境＂的演化将朝着有利于创业投资家的方向发展。金融危机虽然能改变博弈的结果,却未能改变＂囚徒困境＂的成因。     

论魏晋南朝的忠孝实践——以阳翟褚氏为例

忠孝取舍孰先孰后在魏晋南朝时期有一个演变的过程,弃忠从孝是当时社会的趋势和需要。阳翟褚氏在这一时期屡屡背信弃义违背＂忠＂的道德却又信守家族＂孝＂的传统,是当时忠孝实践观的集中代表。     

简论语料库与教师角色之转变

现代电子语料库已有了近50年的发展,但语料库应用于课堂教学才刚刚起步。通过近年语料库辅助课堂教学诸多实践研究,教师们不仅可以获得教学中的例句,还可引导学生们学习更地道、更准确的表达方式。     

浅论高校中国画教学的学生评价机制——多层次、多样性、动态长效教学评价

教学评价是完整教学体系的重要组成部分。根据中国画的艺术特征和教学特点,教师宜采取多层次、多样性、动态长效教学评价机制。教师应当改变单一的教学评价标准,要对不同特点的学生因材施评,以各种形式丰富教学活动,充分发挥评价的教育功能,让教学评价真正促进学生的发展,开掘学生的潜能。     

中国证券业总产出和增加值测算研究

 金融机构总产出和增加值的测算是一项基础性的研究工作。与传统测算方法不同,本文借鉴经济合作和发展组织（OECD）提出的使用者成本法,把证券业视为生产性服务活动,并结合我国的实际情况,从服务的角度初步提出了我国证券业总产出和增加值的测算框架。并提出了未来研究改进的方向。     

Measuring non-linear dependence for two random variables distributed along a curve

We propose new dependence measures for two real random variables not necessarily linearly related. Covariance and linear correlation are expressed in terms of principal components and are generalized for variables distributed along a curve. Properties of these measures are discussed. The new measures are estimated using principal curves and are computed for simulated and real data sets. Finally, we present several statistical applications for the new dependence measures.     

Regularization through variable selection and conditional MLE with application to classification in high dimensions

It is often the case that high-dimensional data consist of only a few informative components. Standard statistical modeling and estimation in such a situation is prone to inaccuracies due to overfitting, unless regularization methods are practiced. In the context of classification, we propose a class of regularization methods through shrinkage estimators. The shrinkage is based on variable selection coupled with conditional maximum likelihood. Using Stein's unbiased estimator of the risk, we derive an estimator for the optimal shrinkage method within a certain class. A comparison of the optimal shrinkage methods in a classification context, with the optimal shrinkage method when estimating a mean vector under a squared loss, is given. The latter problem is extensively studied, but it seems that the results of those studies are not completely relevant for classification. We demonstrate and examine our method on simulated data and compare it to feature annealed independence rule and Fisher's rule.     

Another look at Huber's estimator: A new minimax estimator in regression with stochastically bounded noise

Huber's estimator has had a long lasting impact, particularly on robust statistics. It is well known that under certain conditions, Huber's estimator is asymptotically minimax. A moderate generalization in rederiving Huber's estimator shows that Huber's estimator is not the only choice. We develop an alternative asymptotic minimax estimator and name it regression with stochastically bounded noise (RSBN). Simulations demonstrate that RSBN is slightly better in performance, although it is unclear how to justify such an improvement theoretically. We propose two numerical solutions: an iterative numerical solution, which is extremely easy to implement and is based on the proximal point method; and a solution by applying state-of-the-art nonlinear optimization software packages, e.g., SNOPT. Contribution: the generalization of the variational approach is interesting and should be useful in deriving other asymptotic minimax estimators in other problems.     

On the asymptotic distribution of Cook's distance in logistic regression models

It sometimes occurs that one or more components of the data exert a disproportionate influence on the model estimation. We need a reliable tool for identifying such troublesome cases in order to decide either eliminate from the sample, when the data collect was badly realized, or otherwise take care on the use of the model because the results could be affected by such components. Since a measure for detecting influential cases in linear regression setting was proposed by Cook [Detection of influential observations in linear regression, Technometrics 19 (1977), pp. 15–18.], apart from the same measure for other models, several new measures have been suggested as single-case diagnostics. For most of them some cutoff values have been recommended (see [D.A. Belsley, E. Kuh, and R.E. Welsch, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, 2nd ed., John Wiley & Sons, New York, Chichester, Brisban, (2004).], for instance), however the lack of a quantile type cutoff for Cook's statistics has induced the analyst to deal only with index plots as worthy diagnostic tools. Focussed on logistic regression, the aim of this paper is to provide the asymptotic distribution of Cook's distance in order to look for a meaningful cutoff point for detecting influential and leverage observations.