病例-对照研究中基因型不确定时单倍型关联分析的似然方法

从荧光强度数据出发研究疾病与单倍型之间的相关性.利用基于混合t分布的聚类算法得到了每个个体的所有可能的基因型("基因图谱"), 根据所有这些可能的基因型考虑基于单倍型的logistic回归模型,并且给出了一个研究疾病与单倍型之间相关性的似然方法, 通过进一步的模拟研究发现该似然方法减小了基因型测量误差给单倍型关联分析带来的影响.     

基因组疾病风险单倍型段的置换筛选检验（英文）

通过测试的一群而不是单个的变异与疾病的关联捕获变异的集体行为,文章提出了单倍型关联分析.这种分析通常涉及未知相位多位置基因型在病例和对照组中稀疏频率.它从推断单倍型基因型,共同分类和边际筛查疾病相关的基因单倍型开始.不幸的是,解未知相位的不确定性可能对单倍型共同分类(因此对预测风险单倍型的精度)产生强烈影响.在这里,为了解决这一问题,文章提出一个替代办法:在阶段一中,选择风险基因型而不是共同分类推断单倍型.在阶段二中,从前一阶段选择风险基因型推断出风险单体型.采用仿真研究和实际数据分析评定提议的程序性能.相比现有的多个Z-检验程序,通过使用建议的程序可增加全基因组关联研究的功效.     

单体型推断问题与配对图

纯节俭型单体型推断(PPHI)问题是这样一类单体型推断问题给定n个基因型向量,要求寻找n对单体型,使得每一个基因型刚好由其中一对单体型组合生成,并且这2n个单体型中所含的不同单体型数目最小.u-限制单体型推断(u-PPHI)问题是一类特殊的纯节俭型单体型推断问题,要求每一个单体型至多可以用于分解u个基因型.PPHI和u-PPHI问题都是NP-困难的.文中首先介绍了配对图的概念,并通过配对图将两类问题转化为图论问题;然后分别给出了两类问题的近似算法;最后,专门讨论了当u=2时的2-PPHI问题,并在配对图上给出了相应的算法.     

基于单倍型的复杂疾病基因定位研究

本文讨论了复杂疾病基因定位研究中的单倍型关联分析方法及与之相关的单倍型推断方法,指出了目前已有方法存在的问题与面临的困难,这些困难及问题正是现代统计学研究的热点和亟待解决的问题.通过统计上的思考与理解,提出了一些有待进一步研究的问题.     

不完全资料下logistic回归模型参数估计的EM算法

本文讨论了带有不完全观测协变量的资料的logistic,回归模型。在假设不完全是由于部分观测个体随机缺失(missing at random.MAR)部分协变量的前提下,给出了参数的极大似然估计的EM算法,并导出观测信息阵的具体形式。最后以实例加以验证。     

纵向部分线性模型的分块经验似然的有效推断(英文)

本文考虑了纵向数据的部分线性模型.在考虑个体内部相关性的情况下,研究了回归系数的经验似然推断.对于任意的工作协方差矩阵,所给的经验似然比统计量服从渐近卡方分布,由此可以构造回归参数的置信域.模拟结果表明,在正确指定个体相关结构的情况下,推断的效率会显著的提高.最后,给出了案例分析.     

缺失数据下线性模型的半参数估计

通过比较参数方法和非参数方法对选择概率建模的优缺点,基于充分降维的思想提出了一种利用单指标模型对选择概率建模的半参数方法.基于逆概率加权方法和半参数方法,研究了缺失数据下线性模型的统计推断问题.建立的逆概率加权估计方程可以处理不同的数据缺失情形,给出了线性模型中兴趣参数的估计,并证明了它的渐近正态性.最后通过模拟研究说明提出的方法具有较好的有限样本性质.     

带有辅助信息的多元失效时间数据的估计标准部分似然方法

在许多实际研究中, 由于预算限制, 主协变量值只能对某一个有效集进行准确测量, 但同时对应此主协变量的辅助信息则对全部个体均可以观测. 利用这些辅助协变量的信息有助于提高统计研究的效率. 本文在基于共同基准危险率的边际模型框架下, 我们提出了一些统计推断方法来分析多元失效时间数据. 对于回归参数, 我们提出标准的估计部分似然方程来估计它, 同时也给出了累积基准危险率函数的Breslow 型估计. 得到的估计可以证明是相合的和渐近正态的. 利用模拟分析结果来表明了提出的方法在有限样本下的可行性.     

固定效应部分线性单指标面板模型的惩罚二次推断估计

单指标面板模型已广泛应用于各学科领域的研究中,其估计方法较为丰富,然而鲜有估计方法将个体内的相关性考虑在内.基于此,本文研究了一类个体内存在相关性的固定效应部分线性单指标面板模型,采用惩罚二次推断函数法和LSDV法相结合的方法对模型进行估计,证明了所得估计量的一致性和渐近正态性.Monte Carlo模拟结果显示其具有...     

Buehler置信限的计算

在医学研究和产品研制过程中, 由于试验对象难于找到或者试验费用昂贵常出现小样本情形. 此时, 精确置信推断尤其重要. 只要在样本空间中给出一种序就可以定义模型参数的某个函数的精确置信限. 这样得到的置信限称为Buehler置信限. 虽然它的定义比较容易, 但是当多维参数或者不完全观测数据出现时, 计算有时难于实行. 为了解决这种计算问题, 本文构造出一种基于EM算法的方法. EM算法原本是用于求解极大似然估计的方法, 在这里EM算法首次被用于求解精确置信限. 分析了3种模型和一组实际数据以说明这个方法.     

The complete parsimony haplotype inference problem and algorithms based on integer programming,branch-and-bound and Boolean satisfiability

Haplotype inference by pure parsimony (Hipp) is a well-known paradigm for haplotype inference. In order to assess the biological significance of this paradigm, we generalize the problem of Hipp to the problem of finding all optimal solutions, which we call Chipp. We study intrinsic haplotype features, such as backbone haplotypes and fat genotypes as well as equal columns and decomposability. We explicitly exploit these features in three computational approaches that are based on integer linear programming, depth-first branch-and-bound, and Boolean satisfiability. Further we introduce two hybrid algorithms that draw upon the diverse strengths of the approaches. Our experimental analysis shows that our optimized algorithms are significantly superior to the baseline algorithms, often with orders of magnitude faster running time. Finally, our experiments provide some useful insights into the intrinsic features of this important problem.     

A semiparametric additive rates model for clustered recurrent event data

Recurrent event data often arises in biomedical studies, and individuals within a cluster might not be independent. We propose a semiparametric additive rates model for clustered recurrent event data, wherein the covariates are assumed to add to the unspecified baseline rate. For the inference on the model parameters, estimating equation approaches are developed, and both large and finite sample properties of the proposed estimators are established.     

Haplotyping with missing data via perfect path phylogenies

Computational methods for inferring haplotype information from genotype data are used in studying the association between genomic variation and medical condition. Recently, Gusfield proposed a haplotype inference method that is based on perfect phylogeny principles. A fundamental problem arises when one tries to apply this approach in the presence of missing genotype data, which is common in practice. We show that the resulting theoretical problem is NP-hard even in very restricted cases. To cope with missing data, we introduce a variant of haplotyping via perfect phylogeny in which a path phylogeny is sought. Searching for perfect path phylogenies is strongly motivated by the characteristics of human genotype data: 70% of real instances that admit a perfect phylogeny also admit a perfect path phylogeny. Our main result is a fixed-parameter algorithm for haplotyping with missing data via perfect path phylogenies. We also present a simple linear-time algorithm for the problem on complete data.     

有偏抽样下带信息观察和删失的面板数据的统计分析

面板数据经常出现在许多研究领域, 比如纵向跟踪研究. 在很多情况下, 纵向反应变量与观察 时间和删失时间都有关系. 本文在有偏抽样下, 针对这些相关性存在的情况, 利用一个不能观察的潜在 变量, 提出了一个联合建模方法来刻画纵向反应变量与观察时间和删失时间的相关性, 获得了模型中 回归参数的估计方程以及估计的渐近性质, 并通过数值模拟验证了这些估计在小样本下也是有效的, 同时把该估计方法用于一组实际的膀胱癌数据分析中.     

Haplotyping populations by pure parsimony based on compatible genotypes and greedy heuristics

The population haplotype inference problem based on the pure parsimony criterion (HIPP) infers an m × n genotype matrix for a population by a 2m × n haplotype matrix with the minimum number of distinct haplotypes. Previous integer programming based HIPP solution methods are time-consuming, and their practical effectiveness remains unevaluated. On the other hand, previous heuristic HIPP algorithms are efficient, but their theoretical effectiveness in terms of optimality gaps has not been evaluated, either. We propose two new heuristic HIPP algorithms (MGP and GHI) and conduct more complete computational experiments. In particular, MGP exploits the compatible relations among genotypes to solve a reduced integer linear programming problem so that a solution of good quality can be obtained very quickly; GHI exploits a weight mechanism to selects better candidate haplotypes in a greedy fashion. The computational results show that our proposed algorithms are efficient and effective, especially for solving cases with larger recombination rates.     

Modelling,making inferences and making decisions: The roles of sensitivity analysis

Sensitivity analysis, robustness studies and uncertainty analyses are key stages in the modelling, inference and evaluation used in operational research, decision analytic and risk management studies. However, sensitivity methods -or others so similar technically that they are difficult to distinguish from sensitivity methods- are used in many different circumstances for many different purposes; and the manner of their use in one context may be inappropriate in another. Thus in this paper, I categorise and explore the use of sensitivity analysis and its parallels, and in doing so I hope to provide a guide and typology to a large growing literature.     

On cautious probabilistic inference and default detachment

Conditional probabilities are one promising and widely used approach to model uncertainty in information systems. This paper discusses the DUCK-calculus, which is founded on the cautious approach to uncertain probabilistic inference. Based on a set of sound inference rules, derived probabilistic information is gained by local bounds propagation techniques. Precision being always a central point of criticism to such systems, we demonstrate that DUCK need not necessarily suffer from these problems. We can show that the popular Bayesian networks are subsumed by DUCK, implying that precise probabilities can be deduced by local propagation techniques, even in the multiply connected case. A comparative study with INFERNO and with inference techniques based on global operations-research techniques yields quite favorable results for our approach. Since conditional probabilities are also suited to model nonmonotonic situations by considering different contexts, we investigate the problems of maximal and relevant contexts, needed to draw default conclusions about individuals.