刑事诉讼主体的法理分析--以刑事法律关系为基础

犯罪行为引起的刑事法律关系是考察刑事诉讼主体问题的真正法理基础。国家与被告人作为刑事法律关系的主体是刑事诉讼的当事人 ,被害人及其近亲属不是刑事法律关系的主体 ,故不是刑事诉讼当事人 ,只是发动、支持公法性诉讼的公民追诉人。审判权与公诉权的分立 ,本质上是国家将自身作为自己的对象 ,并在自己(裁判者 )与作为对象的自身 (当事人 )之间建立绝对区分 ,体现了作为公共权力的国家的自我反思节制。其他刑事诉讼主体均不是刑事法律关系的主体 ,按照意志关系或者是国家的代表、被告人和被害人的代理人或帮助人 ,或者是不从属于任何一方的诉讼主体。     

论发达地区农村社会阶层结构的变迁——以绍兴县西蜀阜村为例

此文以绍兴县西蜀阜村为例,基于对大量翔实资料的实证分析,阐述和探讨了经济发达的农村社区在农村工业化和城市化过程中,村落社会结构分化的历史进程、决定因素和现实状况,探讨了阶层分化与农村社会经济变迁的关系,对深化沿海发达地区中心镇村现代化进程的认识,有一定的借鉴意义。     

小议英语无灵句及其翻译

无灵句是英语中一种很重要的句式结构，能否掌握无灵句的特点及其翻译方法直接影响到英语学习者的学习效果。本文主要概括了无灵句的特点及其分类，并且归纳出较为行之有效的翻译方法。     

英语写作中有效反馈信息的探讨

文章简要阐述了反馈信息,论述了作文工作研究的意义及在作文写作中有效的信息反馈形式,提出了增强写作能力的可行措施。     

论奥斯古德对昆明高峣社区的人类学研究

1938年夏天,美国年轻学者科尼利尔斯·奥斯古德为了履行与燕京大学的合作计划,不远万里来到滇池西岸一个名不见经传的小村庄调查.1961年,他开启尘封的笔记本,整理旧资料,补充新文献,完成了<旧中国的农村生活:对云南高峣的社区研究>.70年过去,重温科尼利尔斯·奥斯古德的研究历程,我们仍会从中获得许多有益的启示.     

Two-stage Huber estimation

In this paper we propose a new robust estimator in the context of two-stage estimation methods directed towards the correction of endogeneity problems in linear models. Our estimator is a combination of Huber estimators for each of the two stages, with scale corrections implemented using preliminary median absolute deviation estimators. In this way we obtain a two-stage estimation procedure that is an interesting compromise between concerns of simplicity of calculation, robustness and efficiency. This method compares well with other possible estimators such as two-stage least-squares (2SLS) and two-stage least-absolute-deviations (2SLAD), asymptotically and in finite samples. It is notably interesting to deal with contamination affecting more heavily the distribution tails than a few outliers and not losing as much efficiency as other popular estimators in that case, e.g. under normality. An additional originality resides in the fact that we deal with random regressors and asymmetric errors, which is not often the case in the literature on robust estimators.     

神性、人性和理性--略论艺术形态与时代精神

艺术的本质在于其精神性,从艺术和时代精神的相互关系中去确立划分艺术形态的标准是符合艺术本质的.按照艺术所包含的精神内容的不同,艺术形态可分为神性艺术、人性艺术和理性艺术,以丰富的艺术史例可以说明其特征.     

Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

Approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Results from Kiviet [Kiviet, J. F. (1995), on bias, inconsistency, and efficiency of various estimators in dynamic panel data models, J. Econometrics68:53-78; Kiviet, J. F. (1999), Expectations of expansions for estimators in a dynamic panel data model: some results for weakly exogenous regressors, In: Hsiao, C., Lahiri, K., Lee, L-F., Pesaran, M. H., eds., Analysis of Panels and Limited Dependent Variables, Cambridge: Cambridge University Press, pp. 199-225] are extended to higher-order dynamic panel data models with general covariance structure. The focus is on estimation of both short- and long-run coefficients. The results show that proper modelling of the disturbance covariance structure is indispensable. The bias approximations are used to construct bias corrected estimators which are then applied to quarterly data from 14 European Union countries. Money demand functions for M1, M2 and M3 are estimated for the EU area as a whole for the period 1991: I-1995: IV. Significant spillovers between countries are found reflecting the dependence of domestic money demand on foreign developments. The empirical results show that in general plausible long-run effects are obtained by the bias corrected estimators. Moreover, finite sample bias, although of moderate magnitude, is present underlining the importance of more refined estimation techniques. Also the efficiency gains by exploiting the heteroscedasticity and cross-correlation patterns between countries are sometimes considerable.     

A Bayesian method for classification and discrimination

We discuss Bayesian analyses of traditional normal-mixture models for classification and discrimination. The development involves application of an iterative resampling approach to Monte Carlo inference, commonly called Gibbs sampling, and demonstrates routine application. We stress the benefits of exact analyses over traditional classification and discrimination techniques, including the ease with which such analyses may be performed in a quite general setting, with possibly several normal-mixture components having different covariance matrices, the computation of exact posterior classification probabilities for observed data and for future cases to be classified, and posterior distributions for these probabilities that allow for assessment of second-level uncertainties in classification.     

Generalized Laplacian approximations in Bayesian inference

This paper presents a new Laplacian approximation to the posterior density of η = g(θ). It has a simpler analytical form than that described by Leonard et al. (1989). The approximation derived by Leonard et al. requires a conditional information matrix R_η to be positive definite for every fixed η. However, in many cases, not all R_η are positive definite. In such cases, the computations of their approximations fail, since the approximation cannot be normalized. However, the new approximation may be modified so that the corresponding conditional information matrix can be made positive definite for every fixed η. In addition, a Bayesian procedure for contingency-table model checking is provided. An example of cross-classification between the educational level of a wife and fertility-planning status of couples is used for explanation. Various Laplacian approximations are computed and compared in this example and in an example of public school expenditures in the context of Bayesian analysis of the multiparameter Fisher-Behrens problem.