An Analysis on What Role Population Factors Play for Water Resource Pressure in Beijing

How much water resource pressure is jointly determined by demand and water supply conditions? How much role and influence does the demographic factor play? What is the relationship between the demographic factor and the other driving factors? An in-depth study has a very important significance because the above issues directly related to the future policy formulation with population, economic and resources. Based upon analysis of water resource pressure changing trend in Beijing,the driving factors of the usage of water resources from 2001 to 2010 were analyzed using the LMDI decomposition method. Furthermore, the status and role of demographic factors (including population size,household size and so on) and other various driving factors were studied. The results are shown below: The water resource pressure is not only high, but also continually rising year by year; the greatest growth driving factor of the water usage is GDP per capita growth; the demographic factor (including population size,the amount of family and household size and so on) has some influences on the changes of water usage, but it is not the main factor. Improvement for the utilization of water resources in the productive sector and proper guidance for a rational consumption view are very important to alleviate the water pressure and improve the population carrying capacity.     

意识的强化与中日比较文学研究的再发展

中日文学比较研究首先必须强化理论意识 ,突破实证研究的终点 ,把一种“事实”的证明上升为“意义”的阐释 ,“事实———异同———原因———模式”研究思路才是文学关系研究的完整过程。其次 ,在意义研究中必须强化批判意识 ,确立文化的现代化价值取向。中国在东方文化价值论面前必须保持足够的清醒 :当年“东亚一系”、“同文同种”的文化逻辑最终走向了“大东亚共存共荣“的政治逻辑。第三 ,比较文学的思想本质是对民族主义的超越 ,应强化人类意识 ,以促进文学与思想的世界视野的形成     

论特留份制度

规定特留份制度 ,目的在于保护缺乏劳动能力又没有生活来源的法定继承人的权益。我国继承法也规定了“必要的遗产份额” ,但存在着继承人范围过窄和份额规定不明的缺陷 ,因此作相应的修正非常必要。     

影响中医学现代化的因素浅析

目前中医学现代化的脚步十分缓慢 ,加快中医学现代化已成为刻不容缓的问题。从历史和现实等多方面探讨影响中医学现代化的外部因素和内部因素 ,为加快中医学现代化提供参考     

没有"主体间性"就没有"规则"--论哈贝马斯的规则观

尤根·哈贝马斯的规则观涉及三个理论问题:遵守规则的条件、规则意识的产生和规则正当性的辩护.他把主体间性看作是解决这三个问题的关键:离开了主体间性,就无法知道某人是不是在遵守一条规则;离开了主体间性,就既不能形成"规则意识",也不能从"规则意识"中发展出"原则意识"、分化出"价值意识".离开了主体间性,更无法为规则的正当性提供辩护.哈贝马斯之所以关注规则概念,是因为他关注这样三个实践问题:"区别于自然现象的社会现象的研究何以可能?"、"现代社会中真正自由的人格何以可能?"、"现代社会制度的合法性辩护何以可能?".为了回答这些问题,哈贝马斯用他的交往理性概念来扬弃康德的实践理性概念.把康德关于"无规则即是无理性"的观点和哈贝马斯的"没有主体间性就没有规则"的观点综合起来,可以得出"没有主体间性就没有理性"的结论.     

从清宫戏想到章太炎的“落伍”

基于对风行一时的清宫戏的思考,不禁联想到国学大师章太炎,在目前这个汉民族意识式微的年代,曾被当年革命者认为已经落伍的章太炎的民族主义思想,却很可能对汉民族意识的重新定位有着强烈的现实意义.     

“翻译腔”例析

因理解之误和表达之误所致,"翻译腔"表现各异.作者认为,在英汉翻译中,只有努力钻研原作,充分理解原文的深层含义,熟悉和掌握英汉语言的主要差异,表达符合译语语言习惯,才能使译文流畅自如,提高可接受性,消除"翻译腔".     

加强网络道德建设

随着计算机网络的发展,在21世纪将形成一个与当代工业社会具有不同特征的现代网络社会.现代网络社会作为一个"虚拟"的社会,也是一个充满问题与矛盾的真实社会.它同当代工业社会一样,需要道德建设的支撑.网络道德建设是一项复杂的系统工程.加强网络道德建设具有十分重要的现实意义.     

Regression Analysis of Left-truncated and Case I Interval-censored Data with the Additive Hazards Model

In recent years the analysis of interval-censored failure time data has attracted a great deal of attention and such data arise in many fields including demographical studies, economic and financial studies, epidemiological studies, social sciences, and tumorigenicity experiments. This is especially the case in medical studies such as clinical trials. In this article, we discuss regression analysis of one type of such data, Case I interval-censored data, in the presence of left-truncation. For the problem, the additive hazards model is employed and the maximum likelihood method is applied for estimations of unknown parameters. In particular, we adopt the sieve estimation approach that approximates the baseline cumulative hazard function by linear functions. The resulting estimates of regression parameters are shown to be consistent and efficient and have an asymptotic normal distribution. An illustrative example is provided.     

Model Averaging for Prediction With Fragmentary Data

ABSTRACT

One main challenge for statistical prediction with data from multiple sources is that not all the associated covariate data are available for many sampled subjects. Consequently, we need new statistical methodology to handle this type of “fragmentary data” that has become more and more popular in recent years. In this article, we propose a novel method based on the frequentist model averaging that fits some candidate models using all available covariate data. The weights in model averaging are selected by delete-one cross-validation based on the data from complete cases. The optimality of the selected weights is rigorously proved under some conditions. The finite sample performance of the proposed method is confirmed by simulation studies. An example for personal income prediction based on real data from a leading e-community of wealth management in China is also presented for illustration.