私人妨害原则与相邻权、地役权

文章剖析了英美物权法上“使用权”的概念 ,并解释了财产权就像“权利集束”这个常用比喻 ,并对美国普通法上财产法中的有关相邻方之间土地纠纷的法规进行了比较分析 ,其中特别比较了普通法上的私人妨害原则和中国物权法草案第 9章所规定的相邻权的概念 ,并将普通法体系中的各种私人土地使用协议和地役权与中国物权法草案第 16章的内容进行了对比。     

Expectations, Capital Gains, and Income

A theoretical framework for the measurement of income under uncertainty is developed that addresses some long-standing controversies about the treatment of capital gains. The consequences for economic analysis and policy making are potentially serious, because the treatment of capital gains can significantly affect some major macroeconomic aggregates, including national income and savings, balance of payments deficits, government deficits, and depreciation. (JEL O47 , P44 , Q32 )     

内镜下气囊扩张治疗贲门失弛缓症的近、远期疗效

目的 :回顾气囊扩张治疗贲门失弛缓症的近、远期疗效及治疗过程中的经验。方法 :对经食管吞钡X线拍片确诊的 16例贲门失弛缓症患者 ,采用内镜下气囊扩张治疗并评价其近、远期疗效。结果 :近期疗效 :16例患者中 ,12例 (75 % )显效 ,4例 (2 5 % )有效 ,总有效率为 10 0 % ;远期疗效 :16例患者中 ,1例 2周后复发经再次扩张治疗后未复发 ,另有 1例在治疗后的半年内复发扩张治疗后 ,半年再次复发 ,经直径 4 .0cm气囊扩张后 4年未复发。结论 :气囊扩张治疗贲门失弛缓症有效、易操作、安全、并发症少及复发率低 ,但有较多的细小经验及体会值得总结 ,以便更完善和更成熟。     

Telecommuting: The future of work, corporate culture, and American society

Conclusion Throughout American history, first on the family farm and now in modern day business institutions, the workplace has been a stabilizing institution in American society that has anchored the ongoing, informal interactions occurring outside the workplace. In 1900, few Americans had a private mode of transportation. Barely 4,000 passenger automobiles were sold in 1900, and while horses were common, they were used primarily for farming, public transportation, and commercial activity in cities. By 1930, nearly 40 million cars had been added to America’s streets. The automobile recast life and work in central cities and eventually the suburbs.     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

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.     

Markov chain Monte Carlo methods for high dimensional inversion in remote sensing

Summary.  We discuss the inversion of the gas profiles (ozone, NO₃, NO₂, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the 'Global ozone monitoring of occultation of stars' instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10–20 km. The new feature is that these data allow the inversion of the gas concentration height profiles. A short introduction is given to the present operational data management procedure with examples of the first real data inversion. Several solution options for a more comprehensive statistical inversion are presented. A direct inversion leads to a non-linear model with hundreds of parameters to be estimated. The problem is solved with an adaptive single-step Markov chain Monte Carlo algorithm. Another approach is to divide the problem into several non-linear smaller dimensional problems, to run parallel adaptive Markov chain Monte Carlo chains for them and to solve the gas profiles in repetitive linear steps. The effect of grid size is discussed, and we present how the prior regularization takes the grid size into account in a way that effectively leads to a grid-independent inversion.