基于多维名义模型的加权似然估计方法(英文)

The Monte Carlo study evaluates the relative accuracy of Warm's (1989) weighted likelihood estimate (WLE) compared to the maximum likelihood estimate (MLE) using the nominal response model. And the results indicate that WLE was more accurate than MLE.     

A Generalized Information Criterion for Parameters under an Umbrella Order Restriction

The detection of the configuration of parameters is one of the most important problems in statistical studies. It is well known that the Akaike's information criterion (AIC) is a key tool for this problem     

脂肪族聚碳酸酯(PPC)与聚乳酸(PLA)共混型生物降解材料的热学性能、力学性能和生物降解性研究

通过溶液浇铸法制备了脂肪族聚碳酸酯与聚乳酸的共混物(PPC/PLA).采用示差热分析(DSC)和热重分析(TG)研究了材料的热性能.采用拉伸力学试验研究了共混物的力学性能.通过土壤悬浊拟环境培养降解实验法和扫描电子显微镜分析(SEM)对共混材料的生物降解性能进行了研究.实验结果表明,随着PPC含量的增加,共混物的拉伸强度和杨氏模量降低,而生物降解速率却显著提高.但是,在一定的降解时间内,某些比例共混物的降解速率比100%PPC还要快.综合分析表明,PPC/PLA是力学性能和降解性能可以互补的共混体系.     

玉米淀粉固态电解质质子\电子杂化突触晶体管

随绿色可持续发展观念的深入人心,研究人员致力于寻找天然有机材料应用于功能性电子器件.淀粉以其低廉的价格、丰富的来源和优异的机械性能进入了科研人员的视野.淀粉可由玉米、马铃薯、甘薯和葛根等含淀粉的物质中提取而得,一般不溶于水,在和水加热至一定温度时,则糊化成胶状溶液.本文通过旋涂法将玉米淀粉的胶状溶液旋涂至氧化铟锡玻璃表面,然后在30?C恒温环境中晾干制备成固态胶合状薄膜.以此薄膜作为固态电解质制备了氧化铟锌突触晶体管,并实现了生物神经突触的双脉冲易化、学习记忆能力、高通滤波等可塑性行为的仿真.本研究以玉米淀粉固态胶合薄膜作为电解质大大降低了氧化物薄膜晶体管固态电解质的成本,且该电解质无毒性、来源丰富,将为人工神经网络的开发提供一种可选择的元件.     

PLA,PPC和PHBV共混物的热性能、力学性能和生物降解性能研究

将聚乳酸(PLA)、聚碳酸酯(PPC)及β-羟基丁酸酯与β-羟基戊酸酯共聚物(PHBV)以溶液浇注法制备了各种不同比例的共混膜(60/20/20,40/20/40,40/40/20,20/60/20,20/40/40,20/20/60)。采用示差扫描量热分析(DSC)和热重分析(TG)研究了共混物的热性能,采用万能材料试验机研究了共混物的力学性能,通过土壤悬浊拟环境降解实验和扫描电子显微镜(SEM)研究了共混材料的环境生物降解性能。结果显示,该三元共混体系是部分相容的体系,PLA增加了材料的强度,PPC增加了材料的断裂伸长,PHBV则提高了材料的环境生物降解速率,三者优势互补,是一种有应用前景的生物降解共混体系。     

基于保序回归估计的最大耐受剂量确定方法

I期临床试验研究的主要目标之一是评估药物在不同剂量水平下的毒性, 并且建议一个对病人既安全又有效的剂量, 即最大耐受剂量(MTD). 本文针对拓展的up-and-down设计, 进一步给出其基于保序回归估计的最大耐受剂量确定方法. 经大量模拟, 结果表明: 基于保序回归估计的最大耐受剂量确定方法对推荐MTD的准确性和精确度, 以及保护病人, 防止病人暴露在较高毒性剂量水平下方面实现了有意义的改善.     

伞形序约束下参数的一个广义信息准则

The detection of the configuration of parameters is one of the most important problems im statistical studies. It is well known that the Akaike‘s information criterion (AIC) is a key tool for this problem (see [1]). Usually, the AIC is defined as: AIC(μ^) := l(μ^) -p, where l(μ^) is the log-likelihood with the maximum likelihood estimator (MLE) μ for μ,     

Finite Mixture Normal Models, with Application to Dose-Response Studies

In this paper, we consider the risk assessment problem under multi-levels and multiple mixture subpopulations. Our result is the generalization of the results of [1-5].1 Finite Mixture Normal ModelsIn dose-response studies, a class of phenomena that frequently occur are that experimental subjects (e.g., mice) may have different responses like 'none, mild, severe' after a toxicant experiment, or 'getting worse, no change, getting better' after a medical treatment, etc. These phenomena have attracted the attention of many researchers in recent years. Finite     

基于置信界及信息附加寻找最大耐受剂量的逐步方法

The primary goal of a phase I clinical trial is to find the maximum tolerable dose of a treatment. In this paper, we propose a new stepwise method based on confidence bound and information incorporation to determine the maximum tolerable dose among given dose levels. On the one hand, in order to avoid severe even fatal toxicity to occur and reduce the experimental subjects, the new method is executed from the lowest dose level, and then goes on in a stepwise fashion. On the other hand, in order to improve the accuracy of the recommendation, the final recommendation of the maximum tolerable dose is accomplished through the information incorporation of an additional experimental cohort at the same dose level. Furthermore, empirical simulation results show that the new method has some real advantages in comparison with the modified continual reassessment method.