Optimal designs for threshold-determining limiting dilution assays

In common with other non-linear models, the optimal design for a limiting dilution assay (LDA) depends on the value of the unknown parameter, θ$\theta$, in the model. Consequently optimal designs cannot be specified unless some assumptions are made about the possible values of θ$\theta$. If a prior distribution can be specified then a Bayesian approach can be adopted. A proper specification of the Bayesian approach requires the aim of the experiment to be described and quantified through an appropriate utility function. This paper addresses the problem of finding optimal designs for LDAs when the aim is to determine whether θ$\theta$ is above or below a specified threshold, _θ0${\theta }_0$.     

我国驰名商标反淡化保护立法研究

淡化是一种严重侵害驰名商标权利人利益的侵权行为,我国驰名商标反淡化保护制度在商标淡化行为的概念界定、商标淡化行为的表现形式、认定标准、法律后果的承担等方面还存在立法上的空白。基于对商标权人利益的保护,应当从以下两方面完善驰名商标反淡化立法：将驰名商标淡化理论纳入《商标法》,明确驰名商标淡化的法律性质;明确驰名商标淡化行为人的民事法律责任。     

论驰名商标的反淡化保护

驰名商标淡化行为是一种减少或削弱驰名商标显著性的侵权行为,以制止混淆为基础的传统商标保护理论对此不但无能为力,反而成为认定该种商标侵权的障碍。加强和完善我国驰名商标反淡化保护已不容置疑,因此有必要通过提高驰名商标反淡化保护的立法层次,扩大驰名商标反淡化保护的范围,对未注册商标提供反淡化保护以及将其保护延及网络域名等等进一步完善我国驰名商标反淡化保护制度。     

浅论我国驰名商标的法律保护

驰名商标作为经济生活中重要的元素,是企业通过长期的资源投入而形成的重要的无形资产。驰名商标所代表的商品具有优良的品质,在市场上具有较高的声誉、较强的市场竞争力,能够为企业带来巨大的经济利益,其关键的地位和作用是不言而喻的。因此,研究驰名商标的法律保护问题具有极其重要的理论和现实意义。     

Cystatin C的检测及临床应用

Cystatin C(Cys C)是近年来研究发现的一种特异性高、准确性好、较肌酐清除率更为敏感的评价肾小球滤过率的新指标．Cys C能自由通过肾小球滤过膜，在近曲小管几乎被完全重吸收，然后完全分解代谢，不再重新回到血液循环中去，同时肾小管也不分泌，所以在血液中的浓度较为恒定，成为反映肾小球滤过率的灵敏指标．常用颗粒增强透射免疫比浊法(PETIA)和颗粒增强散射免疫比浊法(PENIA)检测Cys C，操作简单、速度快，在儿科疾病、糖尿病、心血管疾病、肿瘤化疗及肾移植后评估肾小球滤过率等方面具有很高的临床价值．     

弱酸在无限稀释时能完全电离吗?

本文研究了一元弱酸在水中的电离情况．根据物料衡算和近似计算，得到弱酸电离度与Ｋａ的关系式，由此得到：极弱的酸在无限稀释时，不能完全电离．     

Use of Fractals to Estimate Environmental Dilution Factors in River Basins

Every source of liquid pollutants lies somewhere on a watershed. Leachwaters derived from water percolating through solid waste deposits and liquid effluents from industry are diluted on their way down the river until they interact with man or particular environmental systems. This environmental dilution of potentially toxic effluents has to be estimated in assessing health and environmental risks. The problem for the risk assessor is to find an appropriate mathematical model. In this paper, the recent mathematical theory of fractal objects is used to demonstrate that, within certain limits, rivers of all lengths and their river basins are self-similar. Simple general relations exist, therefore, between the length and drainage area of rivers both large and small. These relations and a few additional assumptions are then used to derive an improved set of models for estimating environmental dilution of some pollutants.     

Testing for Serial Correlation in Fixed-Effects Panel Data Models

In this article, we propose various tests for serial correlation in fixed-effects panel data regression models with a small number of time periods. First, a simplified version of the test suggested by Wooldridge (2002) and Drukker (2003) is considered. The second test is based on the Lagrange Multiplier (LM) statistic suggested by Baltagi and Li (1995), and the third test is a modification of the classical Durbin–Watson statistic. Under the null hypothesis of no serial correlation, all tests possess a standard normal limiting distribution as N tends to infinity and T is fixed. Analyzing the local power of the tests, we find that the LM statistic has superior power properties. Furthermore, a generalization to test for autocorrelation up to some given lag order and a test statistic that is robust against time dependent heteroskedasticity are proposed.     

SELECTION OF WEIGHTS FOR WEIGHTED MODEL AVERAGING

We address the task of choosing prior weights for models that are to be used for weighted model averaging. Models that are very similar should usually be given smaller weights than models that are quite distinct. Otherwise, the importance of a model in the weighted average could be increased by augmenting the set of models with duplicates of the model or virtual duplicates of it. Similarly, the importance of a particular model feature (a certain covariate, say) could be exaggerated by including many models with that feature. Ways of forming a correlation matrix that reflects the similarity between models are suggested. Then, weighting schemes are proposed that assign prior weights to models on the basis of this matrix. The weighting schemes give smaller weights to models that are more highly correlated. Other desirable properties of a weighting scheme are identified, and we examine the extent to which these properties are held by the proposed methods. The weighting schemes are applied to real data, and prior weights, posterior weights and Bayesian model averages are determined. For these data, empirical Bayes methods were used to form the correlation matrices that yield the prior weights. Predictive variances are examined, as empirical Bayes methods can result in unrealistically small variances.     

Measuring serial dependence in categorical time series

The analysis of time-indexed categorical data is important in many fields, e.g., in telecommunication network monitoring, manufacturing process control, ecology, etc. Primary interest is in detecting and measuring serial associations and dependencies in such data. For cardinal time series analysis, autocorrelation is a convenient and informative measure of serial association. Yet, for categorical time series analysis an analogous convenient measure and corresponding concepts of weak stationarity have not been provided. For two categorical variables, several ways of measuring association have been suggested. This paper reviews such measures and investigates their properties in a serial context. We discuss concepts of weak stationarity of a categorical time series, in particular of stationarity in association measures. Serial association and weak stationarity are studied in the class of discrete ARMA processes introduced by Jacobs and Lewis (J. Time Ser. Anal. 4(1):19–36, 1983). An intrinsic feature of a time series is that, typically, adjacent observations are dependent. The nature of this dependence among observations of a time series is of considerable practical interest. Time series analysis is concerned with techniques for the analysis of this dependence. (Box et al. 1994p. 1)