网络服务提供者( ISP)信息披露制度——以著作权法领域为中心

在信息技术的发展背景下,网络侵权数量呈现直线飙升.关于网络服务提供者间接侵权责任的建立使权利人确认直接侵权人陷入困境.著作权领域的司法和实践为信息披露制度的建立奠定了良好的基础.立足我国的具体实际和网络现状,信息披露制度应当规定包括网络接入服务提供者在内的网络服务提供者均负有一定的信息披露义务,严格信息披露的条件,采用“通知与反通知”的履行方式,明确网络服务提供者信息披露制度下的责任分配,促使网络服务提供者制定统一的信息披露行业规定.     

货币政策操作工具选择基准的理论分析

中央银行实施货币政策的过程就是运用操作工具实现政策目标的过程，为更好地实现既定的货币政策目标，中央银行应该依据一定的基准选择操作工具。中央银行选择操作工具的基准包括理论基准和实证基准，其中，前者包括外在性基准和内在性基准。内在性基准是其日常操作中最重要的选择基准，它又有主动性基准、微调性基准、信号功能基准、时效性基准和可操作性基准等内容。     

Estimating consumer acceptance limits

A methodology is developed for estimating consumer acceptance limits on a sensory attribute of a manufactured product. In concept these limits are analogous to engineering tolerances. The method is based on a generalization of Stevens' Power Law. This generalized law is expressed as a nonlinear statistical model. Instead of restricting the analysis to this particular case, a strategy is discussed for evaluating nonlinear models in general since scientific models are frequently of nonlinear form. The strategy focuses on understanding the geometrical contrasts between linear and nonlinear model estimation and assessing the bias in estimation and the departures from a Gaussian sampling distribution. Computer simulation is employed to examine the behavior of nonlinear least squares estimation. In addition to the usual Gaussian assumption, a bootstrap sample reuse procedure and a general triangular distribution are introduced for evaluating the effects of a non-Gaussian or asymmetrical error structure. Recommendations are given for further model analysis based on the simulation results. In the case of a model for which estimation bias is not a serious issue, estimating functions of the model are considered. Application of these functions to the generalization of Stevens’ Power Law leads to a means for defining and estimating consumer acceptance limits, The statistical form of the law and the model evaluation strategy are applied to consumer research data. Estimation of consumer acceptance limits is illustrated and discussed.     

Auditing biochemical research data: A case study

There is little documentation in the literature of how to conduct a data audit of laboratory research. The purpose of such an audit is to verify the extent to which the published data agree with the corresponding laboratory notebook data and whether the data thus examined appear to be sufficient to justify the published conclusions. A case study is presented, which demonstrates a step by step approach towards conducting a data audit of a published research study.     

General m-esttmators and applications to bounded influence estimation for non-linear regression

In this paper, we study the M-estimators in the case that λ_F:(β)=E_F:(φ(Z,β))=0 has more than one solution, We show that the numerical iterative procedures converge and that the resulting estimators are consistent and asymptotically normal. We apply them to the non-linear regression models, and then, we find an optimal M-estimate among those that have bounded gross error sensitivity.     

On Calculations Involving the Maximum Cell Frequency

We describe a method of computing the cumulative distribution function of the maximum and minimum cell frequencies in sampling distributions commonly encountered in the analysis of categorical data.The procedure is efficient for exact or approximate calculation in both homogeneous and non-homogeneous cases, is non-recursive, and does not require Dirichlet integrals.Some related statistical problems are also discussed.     

Siting Noxious Facilities: A Test of the Facility Siting Credo

Over the past decade it has become increasingly difficult to site noxious facilities, despite the fact that there is a growing need to do so. To address this problem, a set of guidelines for a fairer, wiser, and more workable siting process—the Facility Siting Credo—was developed during a National Facility Siting Workshop in 1990. This paper presents an empirical test of these guidelines. A questionnaire based on the Credo was completed by stakeholders in 29 waste facility siting cases, both successful and unsuccessful, across the United States and Canada. Using an independent determination of outcome (success), a preliminary ranking of the importance of various Credo principles was obtained. The data reveal that establishing trust between the developer and host community is an important factor in facilitating the siting process. The siting process is most likely to be successful when the community perceives the facility design to be appropriate and to satisfy its needs. Public participation also is seen to be an important process variable, particularly if it encourages a view that the facility best meets community needs. Moreover, a siting process where communities volunteer to host facilities is an approach that holds promise for meeting many of these key success criteria.     

Global and multiple test procedures using ordered p-values—a review

This paper reviews global and multiple tests for the combination ofn hypotheses using the orderedp-values of then individual tests. In 1987, Röhmel and Streitberg presented a general method to construct global level α tests based on orderedp-values when there exists no prior knowledge regarding the joint distribution of the corresponding test statistics. In the case of independent test statistics, construction of global tests is available by means of recursive formulae presented by Bicher (1989), Kornatz (1994) and Finner and Roters (1994). Multiple test procedures can be developed by applying the closed test principle using these global tests as building blocks. Liu (1996) proposed representing closed tests by means of “critical matrices” which contain the critical values of the global tests. Within the framework of these theoretical concepts, well-known global tests and multiple test procedures are classified and the relationships between the different tests are characterised.     

Reverse Regression and Employment Discrimination

The importance of interval forecasts is reviewed. Several general approaches to calculating such forecasts are described and compared. They include the use of theoretical formulas based on a fitted probability model (with or without a correction for parameter uncertainty), various “approximate” formulas (which should be avoided), and empirically based, simulation, and resampling procedures. The latter are useful when theoretical formulas are not available or there are doubts about some model assumptions. The distinction between a forecasting method and a forecasting model is expounded. For large groups of series, a forecasting method may be chosen in a fairly ad hoc way. With appropriate checks, it may be possible to base interval forecasts on the model for which the method is optimal. It is certainly unsound to use a model for which the method is not optimal, but, strangely, this is sometimes done. Some general comments are made as to why prediction intervals tend to be too narrow in practice to encompass the required proportion of future observations. An example demonstrates the overriding importance of careful model specification. In particular, when data are “nearly nonstationary,” the difference between fitting a stationary and a nonstationary model is critical.