亚当斯密的启蒙：政治何居？

在哲人的政治构想中,政治所处的位置能够反映整个图景的内容和面貌。由于政治本身具有秩序的自觉,所以关于政治位置的思考可谓整个思想体系中最为关键、具有决定性的因素。斯密是最伟大的现代启蒙思想家之一,也是现代政治思想巨擘霍布斯的批评者。但他的启蒙采取了一种不同于古典的方式,将现代性进一步推向更深处。斯密的学说不仅照亮了社会,也照亮了人的内心。通过发现无偏旁观者机制,并以之作为整个思想体系的＂万有引力＂,斯密将政治的最高原则自天上拉入人的内心,从而使人心、道德,甚至社会成为一个自治、自为的领域,却又并未逃离政治的视野。斯密是一个系统的政治哲学家。     

Techniques for the analysis of geographic data in evaluation

Although geographic relationships are often an important aspect of a program or system under evaluation, these relationships have frequently been ignored by evaluators. Standard techniques do exist, however, for the analysis of geographic data. This paper presents three such techniques, Geocode Analysis, Trend Surface Analysis, and Social Area Analysis, and discusses their use in evaluation. With these techniques, evaluators can more adequately deal with the questions of geographic relationships which arise in many studies.     

递归预测器神经网络在中国金融市场的实证研究

金融时间序列预测是金融理论领域的研究热点之一。以金融市场中普遍存在的弱混沌为基础,对递归预测器神经网络在中国金融市场的预测应用进行实证研究。在网络训练上,提出用遗传算法优化网络的阈值、权值以及激发函数的幅值和斜率。对国内股票、期货和黄金市场中几个有代表性的品种进行实证检验,计算了预测均方根误差（RMSE）和预测精度（PA）,并和两种典型的神经网络预测模型——BP神经网络、径向基函数神经网络做了比较,结果表明该模型有较好的预测效果。     

SHRINKAGE PREDICTION IN THE EXPONENTIAL DISTRIBUTION WITH A PRIOR INTERVAL FOR THE SCALE PARAMETER

The author proposes the best shrinkage predictor of a preassigned dominance level for a future order statistic of an exponential distribution, assuming a prior estimate of the scale parameter is distributed over an interval according to an arbitrary distribution with known mean. Based on a Type II censored sample from this distribution, we predict the future order statistic in another independent sample from the same distribution. The predictor is constructed by incorporating a preliminary confidence interval for the scale parameter and a class of shrinkage predictors constructed here. It improves considerably classical predictors for all values of the scale parameter within its dominance interval containing the confidence interval of a preassigned level.     

Best Quadratic Unbiased Prediction in a General Linear Model with Stochastic Regression Coefficients

In this article, we discuss on how to predict a combined quadratic parametric function of the form β′ H β + hσ² in a general linear model with stochastic regression coefficients denoted by y  =  X β +  e . Firstly, the quadratic predictability of β′ H β + hσ² is investigated to obtain a quadratic unbiased predictor (QUP) via a general method of structuring an unbiased estimator. This QUP is also optimal in some situations and therefore we hope it will be a fine predictor. To show this idea, we apply the Lagrange multipliers method to this problem and finally reach the expected conclusion through permutation matrix techniques.     

Missing Values in Linear Regression: Imputations Using An Error-Contaminated Linear Predictor

The problem of missing values problem is common in all branches of statistics and especially in regression analysis. Here we consider estimation of the regression parameters in the presence of missingness in the response. The usual method is to replace the missing value by its predicted value based on the available observations without any correction for the disturbance term. Instead we suggest a method which corrects the usual predictor with a guess of the disturbance term based on the available residuals. Comparison between the two methods shows that the latter leads to better results.     

Borrowing strength from past data in small domain prediction by kalman filtering - a case

Point and interval estimators for small domains based exclusively on current and domain specific sample observations are generally ineffective because of inadequate sample-sizes. So, borrowing strength from sample values for analogous domains and simultaneously from all relevant past and auxiliary data is useful in deriving improved small domain statistics. Postulating for simplicity a linear regression model with a single covariate and a zero intercept but a time-specific domain-invariant slope we start with “synthetic” generalized regression predictors for the domain totals. These borrow across only domains. For further improvements a simple autoregressive model is postulated for the slope parameters. Employing Kalman filtering the previous predictors are revised to borrow supplementary strength across time. As drastic simplifying assumptions are needed in such predictions the efficacy of the procedure is examined through an empirical exercise using live data as well as simulations. The numerical findings turn out encouraging.     

论亚当·斯密的正义思想

亚当·斯密的正义思想是其理论的重要组成部分,研究亚当·斯密的正义思想对市场经济体制下如何实现社会正义具有重要的借鉴意义。亚当·斯密的正义思想主要有:制度是正义的保障;效率是正义的标准;自由是正义的核心;同情是正义的矫正。本文对亚当·斯密的正义思想的现代意义与价值进行了评析。