Simple moment-based inferences of generalized concordance correlation

We proposed two simple moment-based procedures, one with (GCCC1) and one without (GCCC2) normality assumptions, to generalize the inference of concordance correlation coefficient for the evaluation of agreement among multiple observers for measurements on a continuous scale. A modified Fisher's Z-transformation was adapted to further improve the inference. We compared the proposed methods with U-statistic-based inference approach. Simulation analysis showed desirable statistical properties of the simplified approach GCCC1, in terms of coverage probabilities and coverage balance, especially for small samples. GCCC2, which is distribution-free, behaved comparably with the U-statistic-based procedure, but had a more intuitive and explicit variance estimator. The utility of these approaches were illustrated using two clinical data examples.     

Large and moderate deviations for modified Engel series

The modified Engel series of real numbers introduced by Rényi (1962 Rényi, A. (1962). A new approach to the theory of Engel’s series. Ann. Univ. Sci. Budapest. Eötvös Sect. Math. 5:25–32. [Google Scholar]) is a simple modification of Engel series, and they have the same classical limit theorems, such as the law of large numbers, central limit theorem, and law of the iterated logarithm. In this paper, we studied the large and moderate deviations for modified Engel series, which indicate that the large deviations for modified Engel series and Engel series are different.     

Parametric and nonparametric methods for confidence intervals and sample size planning for win probability in parallel-group randomized trials with Likert item and Likert scale data

Data on the Likert scale are ubiquitous in medical research, including randomized trials. Statistical analysis of such data may be conducted using the means of raw scores or the rank information of the scores. In the context of parallel-group randomized trials, we quantify treatment effects by the probability that a subject in the treatment group has a better score than (or a win over) a subject in the control group. Asymptotic parametric and nonparametric confidence intervals for this win probability and associated sample size formulas are derived for studies with only follow-up scores, and those with both baseline and follow-up measurements. We assessed the performance of both the parametric and nonparametric approaches using simulation studies based on real studies with Likert item and Likert scale data. The simulation results demonstrate that even without baseline adjustment, the parametric methods did not perform well, in terms of bias, interval coverage percentage, balance of tail error, and assurance of achieving a pre-specified precision. In contrast, the nonparametric approach performed very well for both the unadjusted and adjusted win probability. We illustrate the methods with two examples: one using Likert item data and the other using Like scale data. We conclude that non-parametric methods are preferable for two-group randomization trials with Likert data. Illustrative SAS code for the nonparametric approach using existing procedures is provided.     

LEVERAGE ADJUSTMENTS FOR DISPERSION MODELLING IN GENERALIZED NONLINEAR MODELS

For normal linear models, it is generally accepted that residual maximum likelihood estimation is appropriate when covariance components require estimation. This paper considers generalized linear models in which both the mean and the dispersion are allowed to depend on unknown parameters and on covariates. For these models there is no closed form equivalent to residual maximum likelihood except in very special cases. Using a modified profile likelihood for the dispersion parameters, an adjusted score vector and adjusted information matrix are found under an asymptotic development that holds as the leverages in the mean model become small. Subsequently, the expectation of the fitted deviances is obtained directly to show that the adjusted score vector is unbiased at least to O(1/n) . Exact results are obtained in the single‐sample case. The results reduce to residual maximum likelihood estimation in the normal linear case.     

转基因主粮商业化对现行法律的挑战与对策分析

转基因主粮的商业化是涉及国计民生的重大问题,舆论上对其是否商业化争论不休,少有研究涉及当前法律在商业化情形下存在的巨大不足。现行的转基因法律、法规和规章在法律理念、法律制度、法律程序和法律救济方面等存在诸多问题。转基因作为生产过程,在区分困难的客观特性和主粮问题上根本利益的严重冲突导致转基因立法的举步维艰。转基因主粮商业化决策之前,必须先进行法律的整体更新和完善,包括转变立法理念,确定法律原则,通过专门立法、革新法律制度,完善信息公开、增加公众参与,加重违法成本、明确侵权责任等。总之,我国目前现尚不具备转基因主粮商业化的法律环境,贸然推行商业化,将带来严重危害后果。     

美欧日转基因食品安全管理对我国的重要启示

通过对美欧日转基因食品管理理念、管理机构、标签制度和立法情况的对比研究,以及对中国转基因食品安全管理现状的分析,中国转基因食品安全管理要借鉴美欧日的经验,完善国家转基因食品安全管理法律法规体系,促成《转基因食品安全法》的出台;加强管理制度建设,对转基因食品进行全面细致的规范;优化管理机构的设置,建立行之有效的管理体制;积极参与国际合作,逐步与国际上通行的标准和做法接轨.     

更改条件/判定覆盖最小真值表生成算法及其应用

研究了从一个布尔表达式生成最小真值表的算法，阐述了在覆盖测试工具中利用此算法辅助设计测试用例的应用问题，以此提高代码覆盖率．     

转基因作物环境风险争论综述

科学家在转基因作物环境风险问题上存在着激烈的争论。一种观点认为,转基因作物不比传统农作物具有更多的环境风险;而另一种观点认为,转基因作物存在着不可忽视的潜在的环境风险。持不同观点的科学家在转基因作物是否会产生超级杂草,是否会产生超级害虫,是否会影响生物多样性,是否会产生二次生态影响等环境风险问题上,有着不同的看法。由于科学知识的有限性、生态环境的复杂性以及转基因技术的新颖性,科学在转基因作物环境风险问题上还没有定论。而加强转基因技术的研究,完善评价转基因技术环境风险的理论与方法,制定出科学、合理的环境风险应对措施,就显得十分重要和紧迫。     

大国开放条件下转基因棉花研发福利效应研究

采用大国开放条件下农业研发经济剩余模型对1996-2008年间转基因棉花研发福利效应进行研究。结果表明：因技术进步所产生的＂农业踏车效应＂朝不利于农民福利的方向发展,包括我国棉农在内的各国棉花生产者都因种植转基因棉花而在经济利益上遭受显著损失;消费者是新技术的最大获益者,并且随着转基因棉花采用率的提高其将获益更大,而研发者也因知识产权保护获得可观的技术使用费;对一国整体而言,以中国和印度为代表的发展中国家是新技术的最大获益者。     

复合改性水玻璃粘结剂砂热芯盒法的试验与探讨

采用复合改性水玻璃粘结剂进行热芯盒法模拟试验和生产试验，试验结果表明，只要有合适的固化催化剂和合理的混砂及射制工艺，4#粘结剂砂完全可以取代树脂粘结剂砂进行热芯盒法制芯。