可交换条件下多维结构回归模型总体平均处理效应的估计

在可交换条件下，当响应变量为多维时，利用结构回归模型研究总体平均处理效应的估计。     

基准剂量软件BMDS中连续模型的研究

对美国环境保护署开发的基准剂量软件BMDS的2.1.1版中连续模型进行了剖析,详细介绍了模型形式;参数初始值的设置、估计方法、标准误、置信上下限;全模型、残差模型和拟合模型的对数似然函数、模型的显著性检验;各剂量下概率的预测、残差;两种风险模式下基准剂量的计算方法,并将计算结果与BMDS软件的计算结果进行比较,结果表明本文介绍的计算方法的正确性,为下一步进行软件设计奠定了理论基础.     

基于密码子偏性的置换模型估计正向选择

由于遗传密码子的简并性特征,大多氨基酸由多于一种密码子编码.在蛋白质编码过程中,同义密码子间的使用有着较显著的偏差,即同义密码于使用频率不等.应用CUSP软件对数据集H3N2和MHC进行同义密码子使用偏性的分析,然后基于同义密码子的使用偏性建立新的密码子置换模型,并在此模型的基础上分析物种的正向选择性.分析结果表明新的密码子置换模型能更好地拟合数据,由此可得到更加可靠的参数估计值.     

非线性再生散度随机效应模型的极大似然估计及随机逼近算法

非线性再生散度随机效应模型包括了非线性随机效应模型和指数族非线性随机效应模型等．通过视模型中的随机效应为假想的缺失数据和应用Metropolis-Hastings(简称MH) 算法,提出了模型参数极大似然估计的随机逼近算法．模拟研究和实例分析表明了该算法的可行性．     

多维协变量具有测量误差的结构回归模型

提出具有测量误差的结构回归模型,研究可交换条件下多维协变量的测量误差对平均处理效应估计的影响,在没有其它的附加条件下,尽管大多数模型参数不可识别,平均处理效应仍可识别,由于平均处理效应的极大似然估计求解困难,建议在实际中使用拟极大似然估计作为替代。     

非线性再生散度随机效应模型的极大似然估计及EM算法

非线性再生散度随机效应模型是指数族非线性随机效应模型和非线性再生散度模型的推广和发展．通过视模型中的随机效应为假想的缺失数据和应用Metropolis-Hastings（MH）算法,提出了模型参数极大似然估计的Monte-Carlo EM（MCEM）算法,并用模拟研究和实例分析说明了该算法的可行性．     

总体平均处理效应的拟极大似然估计

本文给出可交换条件下多维协变量的带有测量误差的多维结构回归模型,利用该模型研究总体平均处理效应的估计,给出当暴露组和对照组的协变量测量误差同分布时总体平均处理效应的拟极大似然估计及其性质．     

单个协变量带有测量误差的多维结构回归模型

给出可交换条件下单个协变量的带有测量误差的多维结构回归模型,利用该模型研究总体平均处理效应的估计,给出当暴露组和对照组的协变量测量误差同分布时总体平均处理效应的拟极大似然估计及其性质.     

反应扩散模型的全局渐近稳定性和持续性

本文主要应用反应扩散方程的不变原理和构造适当的Liapunov泛函方法研究了一类反应扩散模型的唯一齐次正平衡态的全局渐近稳定性.进一步,根据Dunbar et al.(1986)和Hutson与Moran(1987)得出的动力学系统结果,我们得到了判别具有持续性的充分条件.     

光合电子流对光响应的机理模型及其应用

光合电子流对光响应的机理可以揭示植物光合电子流与光强、植物捕光色素分子物理特性之间的关系。该文讨论了光合电子流对光响应的机理模型的特性以及捕光色素分子的物理性质, 并利用此模型拟合了山莴苣(Lagedium sibiricum)、一年蓬(Erigeron annuus)和紫菀(Aster tataricus)的光合电子流对光响应的曲线。由此模型不仅可以得到植物的最大光合电子流、饱和光强、初始斜率等参数, 还可以获得捕光色素分子有效光能吸收截面和处于最低激发态的捕光色素分子数对光的响应关系。结果表明: 随光强的增加, 山莴苣的捕光色素分子的有效光能吸收截面下降最快, 紫苑的下降速度最慢; 山莴苣处于最低激发态的捕光色素分子数增长速度最快, 紫苑的增长速度最小。捕光色素分子的有效光能吸收截面随光强增加而下降、处于最低激发态的捕光色素分子数随光强增加而增加的特性将减少其光能的吸收和激子的传递, 因而有利于减少强光对植物产生的光伤害。     

A Rapid and Efficient Estimation Procedure for the Negative Binomial Distribution

This paper presents a new noniterative procedure for estimating the parameters of a negative binomial distribution. The procedure uses the first moment equation and an equation based on the weighted sample mean, with weights ω_x∝ α^z. The selection of a value for α is examined. A simulation study has been carried out and also the method has been applied to the 35 data sets analysed by Martin and Katti (1965, Biometrics) in order to compare it with the method of moments and with the method of maximum likelihood (ML). We conclude that the new procedure has greater relative efficiency than the method of moments; it gives estimates which are consistently close to ML and are easy to calculate.     

Sparse Sampling and Maximum Likelihood Estimation for Boolean Models

A condition for practical independence of contact distribution functions in Boolean models is obtained. This result allows the authors to use maximum likelihcod methods, via sparse sampling, for estimating unknown parameters of an isotropic Boolean model. The second part of this paper is devoted to a simulation study of the proposed method. AMS classification: 60D05     

Asymptotic behavior of the scaled mutation rate estimators

Important aspects of population evolution have been investigated using nucleotide sequences. Under the neutral Wright–Fisher model, the scaled mutation rate represents twice the average number of new mutations per generations and it is one of the key parameters in population genetics. In this study, we present various methods of estimation of this parameter, analytical studies of their asymptotic behavior as well as comparisons of the distribution's behavior of these estimators through simulations. As knowledge of the genealogy is needed to estimate the maximum likelihood estimator (MLE), an application with real data is also presented, using jackknife to correct the bias of the MLE, which can be generated by the estimation of the tree. We proved analytically that the Waterson's estimator and the MLE are asymptotically equivalent with the same rate of convergence to normality. Furthermore, we showed that the MLE has a better rate of convergence than Waterson's estimator for values of the parameter greater than one and this relationship is reversed when the parameter is less than one.     

A concept of a general computer program for the iterative calculation of maximum likelihood estimators with application to qualitative dose response analyses

In this article we describe the construction of a general computer program for the iterative calculation of maximum likelihood estimators. The program is general in the sense that it allows the maximization of any given likelihood function. The user only has to write a subroutine LKLHD, in which the special likelihood function and their first and second derivatives will be calculated. This subroutine is an input parameter of the optimization program. This enables the user to employ one main program for the maximization of various likelihood functions. This advantage will be shown for the evaluation of qualitative dose response relationships (quantal assays: probit-, logit-analysis).