商标权与企业名称权的法律冲突以及解决方案

商标权与企业名称权是企业的两项重要知识产权 ,二者在实践中发生的冲突 ,已经引起人们的重视。目前发达国家以及一些重要的国际公约建立了商标反淡化的法律制度 ,仍没有彻底解决企业的商标权和名称权的法律冲突问题。我国应当根据自己的国情 ,进一步完善驰名商标的法律制度 ,同时通过取消现行关于企业名称的不合理的规定 ,建立企业名称和商标的交叉管理 ,以便从根本上解决这一法律冲突 ,建立公正、公平的市场经济秩序。     

Testing for pairwise serial independence via the empirical distribution function

Built on Skaug and Tjøstheim's approach, this paper proposes a new test for serial independence by comparing the pairwise empirical distribution functions of a time series with the products of its marginals for various lags, where the number of lags increases with the sample size and different lags are assigned different weights. Typically, the more recent information receives a larger weight. The test has some appealing attributes. It is consistent against all pairwise dependences and is powerful against alternatives whose dependence decays to zero as the lag increases. Although the test statistic is a weighted sum of degenerate Cramér–von Mises statistics, it has a null asymptotic N (0, 1) distribution. The test statistic and its limit distribution are invariant to any order preserving transformation. The test applies to time series whose distributions can be discrete or continuous, with possibly infinite moments. Finally, the test statistic only involves ranking the observations and is computationally simple. It has the advantage of avoiding smoothed nonparametric estimation. A simulation experiment is conducted to study the finite sample performance of the proposed test in comparison with some related tests.     

Testing serial correlation in partially linear models with validation data

This article investigates the testing for serial correlation in partially linear models with validation data and applies the empirical likelihood methods to construct serial tests statistics, and then we derive the asymptotic distribution of the test statistics under null hypothesis. Simulation results show that our method performs well.     

Generalized least squares with misspecified serial correlation structures

Summary. The regression literature contains hundreds of studies on serially correlated disturbances. Most of these studies assume that the structure of the error covariance matrix Ω is known or can be estimated consistently from data. Surprisingly, few studies investigate the properties of estimated generalized least squares (GLS) procedures when the structure of Ω is incorrectly identified and the parameters are inefficiently estimated. We compare the finite sample efficiencies of ordinary least squares (OLS), GLS and incorrect GLS (IGLS) estimators. We also prove new theorems establishing theoretical efficiency bounds for IGLS relative to GLS and OLS. Results from an exhaustive simulation study are used to evaluate the finite sample performance and to demonstrate the robustness of IGLS estimates vis-à-vis OLS and GLS estimates constructed for models with known and estimated (but correctly identified) Ω. Some of our conclusions for finite samples differ from established asymptotic results.     

Confidence Bands for ROC Curves With Serially Dependent Data

We propose serial correlation-robust asymptotic confidence bands for the receiver operating characteristic (ROC) curve and its functional, viz., the area under ROC curve (AUC), estimated by quasi-maximum likelihood in the binormal model. Our simulation experiments confirm that this new method performs fairly well in finite samples, and confers an additional measure of robustness to nonnormality. The conventional procedure is found to be markedly undersized in terms of yielding empirical coverage probabilities lower than the nominal level, especially when the serial correlation is strong. An example from macroeconomic forecasting demonstrates the importance of accounting for serial correlation when the probability forecasts for real GDP declines are evaluated using ROC. Supplementary materials for this article are available online.     

选择集同异结构对消费者决策冲突的影响研究

属性的同异会对消费者的风险感知及信息选择产生影响,由此产生趋同属性的风险稀释效应和趋异属性的知觉聚焦效应。在不同的风险感知情形下,这2种效应的强弱表现会有所差异,从而会对趋同属性产品和趋异属性产品的吸引力评价有所影响,也会影响到消费者的决策困难度。研究发现,在一个其他属性值相同,但某信息不完全的属性值不同的2个方案构成的选择集内,加入一个和其中一个方案属性相同或类似的方案,无论高风险感知程度的高低,都会导致选择困难度的降低,同时,在感知风险为中和高时,属性趋同产品的吸引力要高于属性趋异产品的吸引力,但在感知风险低时,二者的吸引力无显著差异。     

商标的混淆、联想、淡化及其相关理论之比较探析

现代社会,商品竞争逐渐异化为商标的竞争,这种竞争趋势促进了商标功能的变化,使得商标的功能从单纯的识别作用向表彰作用扩张,导致了传统的商标混淆理论向联想、淡化理论的发展。因此,商标侵权理论的发展是一个从防止商品混淆到关注全面保护商标的过程。淡化不仅侵害商标的显著性和识别性,同时亦侵害了商标所体现的商誉。     

Ramifications of Risk Measures in Implementing Quantitative Performance Assessment for the Proposed Radioactive Waste Repository at Yucca Mountain, Nevada, USA

As part of its preparation to review a potential license application from the U.S. Department of Energy (DOE), the U.S. Nuclear Regulatory Commission (NRC) is examining the performance of the proposed Yucca Mountain nuclear waste repository. In this regard, we evaluated postclosure repository performance using Monte Carlo analyses with an NRC-developed system model that has 950 input parameters, of which 330 are sampled to represent system uncertainties. The quantitative compliance criterion for dose was established by NRC to protect inhabitants who might be exposed to any releases from the repository. The NRC criterion limits the peak-of-the-mean dose, which in our analysis is estimated by averaging the potential exposure at any instant in time for all Monte Carlo realizations, and then determining the maximum value of the mean curve within 10000 years, the compliance period. This procedure contrasts in important ways with a more common measure of risk based on the mean of the ensemble of peaks from each Monte Carlo realization. The NRC chose the former (peak-of-the-mean) because it more correctly represents the risk to an exposed individual. Procedures for calculating risk in the expected case of slow repository degradation differ from those for low-probability cases of disruption by external forces such as volcanism. We also explored the possibility of risk dilution (i.e., lower calculated risk) that could result from arbitrarily defining wide probability distributions for certain parameters. Finally, our sensitivity analyses to identify influential parameters used two approaches: (1). the ensemble of doses from each Monte Carlo realization at the time of the peak risk (i.e., peak-of-the-mean) and (2). the ensemble of peak doses calculated from each realization within 10000 years. The latter measure appears to have more discriminatory power than the former for many parameters (based on the greater magnitude of the sensitivity coefficient), but can yield different rankings, especially for parameters that influence the timing of releases.     

A generalized estimating equation approach to modelling incompatible data formats with covariate measurement error: application to human immunodeficiency virus immune markers

The integration of technological advances into research studies often raises an issue of incompatibility of data. This problem is common to longitudinal and multicentre studies, taking the form of changes in the definitions, acquisition of data or measuring instruments of some study variables. In our case of studying the relationship between a marker of immune response to human immunodeficiency virus and human immunodeficiency virus infection status, using data from the Multi-Center AIDS Cohort Study, changes in the manufactured tests used for both variables occurred throughout the study, resulting in data with different manufactured scales. In addition, the latent nature of the immune response of interest necessitated a further consideration of a measurement error component. We address the general issue of incompatibility of data, together with the issue of covariate measurement error, in a unified, generalized linear model setting with inferences based on the generalized estimating equation framework. General conditions are constructed to ensure consistent estimates and their variances for the primary model of interest, with the asymptotic behaviour of resulting estimates examined under a variety of modelling scenarios. The approach is illustrated by modelling a repeated ordinal response with incompatible formats, as a function of a covariate with incompatible formats and measurement error, based on the Multi-Center AIDS Cohort Study data.     

Tumour incidence, prevalence and lethality estimation in the absence of cause-of-death information

Summary.  A Bayesian intensity model is presented for studying a bioassay problem involving interval-censored tumour onset times, and without discretization of times of death. Both tumour lethality and base-line hazard rates are estimated in the absence of cause-of-death information. Markov chain Monte Carlo methods are used in the numerical estimation, and sophisticated group updating algorithms are applied to achieve reasonable convergence properties. This method was tried on the rat tumorigenicity data that have previously been analysed by Ahn, Moon and Kodell, and our results seem to be more realistic.