更敦群培和季羡林——学术道路和命运的异同

更敦群培与季羡林是当代中国两位差不多同时期的学术大师,不论从求学的经历,还是学术方向的选择方面均有相似之处.但是,季先生的学术生涯一直持续了60多年,并成为享誉海内外的学术大师,而更敦群培虽对近代藏族思想启蒙发挥较大的作用,但是其生命的最后3年却是在铁窗中度过,并最终在49岁时含恨逝于拉萨.本文试图通过资料对比的方式,找到两人学术道路的相似点与不同点,并对两人最终命运的反差做一些分析,以探究不同的社会环境对两人学术道路选择所造成的影响.     

民族自治地方经济发展与公民收入改善的制度约束研究

尽管,近年来民族自治地方公民的实际支付能力得到了极大提升,但与全国平均水平相比还明显偏低。其中,第一产业的产值结构与就业结构、家庭联产承包责任制与农业产业化规模化经营、生态保护与农民增收的关系没有得到妥善处理是差距过大的根本原因;在自然地理环境、历史发展水平约束下,民族自治地方政府没有切实履行其承担的促进区域经济平衡发展的职责是差距过大的主要根源;合理分配制度的缺乏、国家针对特定群体的福利政策和行业垂直管理体制下不同工资制度的实施是差距过大的主要原因。针对上述原因,采取针对性对策措施可以有效促进民族自治地方公民收入的改善。     

四川少数民族人口发展路径探析

从人口数量、质量、结构三个方面分析四川少数民族人口发展现状,发现少数民族人口发展与区域资源环境、经济社会发展不协调带来了诸多问题,如人口增长过快导致的人均耕地锐减、环境恶化、就业压力增大,人口质量难以满足经济社会发展的需要,人口城市化滞后的消极效应等。为此,实现少数民族人口现代化的路径需控制人口数量、提高人口素质、推进人口城市化。     

生物性和社会文化性:对“家族”与“民族”关系的人类学思考

对生物性与社会文化性的讨论是人类学研究的基本理论命题之一。从这两个层面去看待家族和民族,既避免了封闭的阈于血缘和文化等传统特征的研究,又避免了在全球化背景下过度突出家族主义和民族主义政治意识形态的倾向,从该二元概念为切入点探讨中国人类学研究中不可忽视的家族和民族关系问题,揭示二者的相似性,有助于重新思考文化生产、文化再造、家族主义和民族主义等问题。     

An Extension of Economic Design of x-Bar Control Charts for Non Normally Distributed Data Under Weibull Shock Models

Three parameters—sample size, sampling intervals, and the control limits—must be determined when the x bar chart to monitor a manufacturing process. The constant sampling intervals were widely employed because of its administrative simplicity. However, the variable sampling interval (VSI) has recently been shown to give substantially faster detection of most process shifts than fixed-sampling-interval (FSI) for x-bar charts. In addition, these measurements in the subgroup are assumed to be normally distributed. That assumption may not be tenable. This investigation compares the economic design of x-bar control charts for non normal data under Weibull shock models with various sampling avenues.     

On the Bias of the Maximum Likelihood Estimator for the Two-Parameter Lomax Distribution

The Lomax (Pareto II) distribution has found wide application in a variety of fields. We analyze the second-order bias of the maximum likelihood estimators of its parameters for finite sample sizes, and show that this bias is positive. We derive an analytic bias correction which reduces the percentage bias of these estimators by one or two orders of magnitude, while simultaneously reducing relative mean squared error. Our simulations show that this performance is very similar to that of a parametric bootstrap correction based on a linear bias function. Three examples with actual data illustrate the application of our bias correction.     

Empirical Analysis of Interval-Censored Failure Time Data with Linear Transformation Models

Interval-censored data naturally arise in many studies. For their regression analysis, many approaches have been proposed under various models and for most of them, the inference is carried out based on the asymptotic normality. In particular, Zhang et al. (2005) discussed the procedure under the linear transformation model. It is well-known that the symmetric property implied by the normal distribution may not be appropriate sometimes. Also the method could underestimate the variance of estimated parameters. This paper proposes an empirical likelihood-based procedure for the problem. Simulation and the analysis of a real data set are conducted to assess the performance of the procedure.     

Semiparametric Smooth Coefficient Stochastic Frontier Model With Panel Data

ABSTRACT

We investigate the semiparametric smooth coefficient stochastic frontier model for panel data in which the distribution of the composite error term is assumed to be of known form but depends on some environmental variables. We propose multi-step estimators for the smooth coefficient functions as well as the parameters of the distribution of the composite error term and obtain their asymptotic properties. The Monte Carlo study demonstrates that the proposed estimators perform well in finite samples. We also consider an application and perform model specification test, construct confidence intervals, and estimate efficiency scores that depend on some environmental variables. The application uses a panel data on 451 large U.S. firms to explore the effects of computerization on productivity. Results show that two popular parametric models used in the stochastic frontier literature are likely to be misspecified. Compared with the parametric estimates, our semiparametric model shows a positive and larger overall effect of computer capital on the productivity. The efficiency levels, however, were not much different among the models. Supplementary materials for this article are available online.     

STAR非线性平稳性检验中误设定的伪检验研究

本文通过理论分析和蒙特卡洛仿真模拟,研究平稳性检验中选用的统计量与数据生成过程不一致时,非线性ESTAR、LSTAR与线性DF检验法能否得出正确的结论.研究表明,二阶LSTAR与ESTAR模型可用相同的检验方法,但前者的非线性特征更强.当数据生成过程为线性AR,或非线性ESTAR、二阶LSTAR模型时,使用DF或ESTAR检验法可得出大致正确的结论,但LSTAR检验法完全失败.数据生成过程的非线性特征越强,ESTAR较DF检验方法的功效增益越高;线性特征越强,DF的功效增益越高.当转移函数F(θ,c,zt)中θ较大导致一阶泰勒近似误差较大或c非0时,标准ESTAR与LSTAR非线性检验法失去应用条件.θ较大或c偏离0较远时,数据生成过程中线性成分增强,用线性DF检验可获得更好的检验结果.