基准剂量软件BMDS中基准剂量下限的计算研究

基准剂量下限作为风险评估的参考点,是剂量评估的重要参数.对美国环境保护暑开发的基准剂量软件BMDS的2.2版中采用的似然比法计算基准剂量下限进行了研究,以二分Logistic模型为例,详细剖析了这种计算方法的运行机制,并将计算结果和BMDS软件的计算结果进行比较,验证了本文介绍方法的正确性.采用这种计算方法设计开发了我国的二分型基准剂量模型评估软件.     

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。     

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。     

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.     

脑磁源的定位研究

在脑电脑磁研究中,常将大脑视为一电磁系统,利用准静态近似下的麦克斯韦方程,可以发现代表脑神经元活动的原在电流密度与脑外磁场之间呈线性关系。在高斯哭喊杨存在的情况下,采用极大似然估计理论,讨论了一种基于脑磁场时域－罕注解磁源定位问题的一般方法。球对称导体模型下的模拟计算表明,这一方法是有效的。对于考虑真实头模型下的磁源定位问题求解磁源定位,提出了一种联合使用脑磁脑电数据的近似方法。     

多重二元响应Probit模型的渐近有效估计

针对多重二元响应Probit模型提出了两步估计方法,第一步由边际似然得到参数√n相合的估计,第二步通过一步迭代得到渐近有效估计,由于只需一步迭代,因此在利用模拟方法计算信息阵时,可以增加模拟的次数,从而减少模拟所产生的扰动对估计的影响．     

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.     

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．     

主基因-多基因混合遗传分析中的EM算法

大量试验数据和QTL作图结果表明：控制数量性状的基因中既有遗传效应较大的主基因,又有遗传效应较小的多基因,其分离世代表现出多峰性,即出现多个分布混合的特征．本文研究利用混合分布理论鉴定和分析主基因-多基因混合遗传模型的具体方法,推导了鉴定主基因存在和多基因存在的EM算法．以大豆开花期性状为例说明了该方法的应用,在所分析的两个杂交组合的F_2世代数据中均发现有主基因的存在、骨绿豆×泰兴黑豆杂交组合中主基因几乎不存在显性,骨绿豆×上海红芒早杂交组合中主基因（晚开花）表现出完全显性,并且有多基因存在.     

混合分布理论及应用

本文介绍了混合分布的历史及其一般理论．在阐述混合分布理论在医学、农业、渔业、地理学等方面的应用以及在统计学其它有关分支中的应用的基础上,还详细讨论了这一理论可能在遗传分析中的作用,并提出一些值得进一步研究的问题．     

Boolean Models: Maximum Likelihood Estimation from Circular Clumps

This paper deals with the problem of making inferences on the maximum radius and the intensity of the Poisson point process associated to a Boolean Model of circular primary grains with uniformly distributed random radii. The only sample information used is observed radii of circular clumps (DUPAC, 1980). The behaviour of maximum likelihood estimation has been evaluated by means of Monte Carlo methods.     

Fitting Regression Models to Ordinal Data

This paper deals with the analysis of ordinal data by means of a threshold model. Maximum likelihood estimation is discussed and two examples are used to illustrate the methods.     

Maximum Likelihood Neural Network Prediction Models

Neural networks have received much attention in recent years mostly by non-statisticians. The purpose of this paper is to incorporate neural networks in a non-linear regression model and obtain maximum likelihood estimates of the network parameters using a standard Newton-Raphson algorithm. We use maximum likelihood estimators instead of the usual back-propagation technique and compare the neural network predictions with predictions of quadratic regression models and with non-parametric nearest neighbor predictions. These comparisons are made using data generated from a variety of functions. Because of the number of parameters involved, neural network models can easily over-fit the data, hence validation of results is crucial.     

Zero-Altered and other Regression Models for Count Data with Added Zeros

On occasion, generalized linear models for counts based on Poisson or overdispersed count distributions may encounter lack of fit due to disproportionately large frequencies of zeros. Three alternative types of regression models that utilize all the information and explicitly account for excess zeros are examined and given general formulations. A simple mechanism for added zeros is assumed that directly motivates one type of model, here called the added-zero type, particular forms of which have been proposed independently by D. LAMBERT (1992) and in unpublished work by the author. An original regression formulation (the zero-altered model) is presented as a reduced form of the two-part model for count data, which is also discussed. It is suggested that two-part models be used to aid in development of an added-zero model when the latter is thought to be appropriate.     

具有周期系数和连续时滞的扩散模型的周期解

本文讨论了具有周期系数和连续时滞的竞争扩散模型,得到了保证其存在唯一周期解及全局渐近稳定的充分条件．     

Small Sample Properties of the Likelihood Estimator in Proportional Hazard Regression Models with Omitted Variables

The paper deals with the effects of incorrectly omitted regressor variables in a parametric proportional hazard regression model. By studying conditions for equality between the estimators of correct and incorrect models it is demonstrated analytically that such cases are not to be expected in practise. A small sample Monte Carlo experiment indicates severe negative effects on the retained parameters both in terms of bias and mean square error.     

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     

A Note on Maximum Likelihood Ratio Test of No Outliers in Regression Models

A derivation of the maximum likelihood ratio test for testing no outliers in regression models is given using the method of WETHERILL (1981, pp. 106–107) for estimating the regression parameters. This method is essentially similar to the one outlined in BARNETT and LEWIS (1978, p. 263), although by our detailed derivation it is easier to see that the maximum likelihood estimate of θ of model (3) under the hypothesis that the i^th observation in an outlier is the same as that obtained from model (1) when the i^th observation is removed.     

The General Linear Model as a Framework for Models in Configural Frequency Analysis

The present paper discusses models of Configural Frequency Analysis (CFA). For most models of CFA maximum likelihood estimators are given. For all of these models least squares estimators are also given. These estimators are equivalent to each other if quasiparametric conditions prevail. Using the second approach, the general linear model can be used to systematize CFA models. Numerical examples are given, using both artificial and psychiatric data.