A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.     

未结盟大学的联盟

通过梳理管理学界对战略联盟的定义，发现价值链的鸸释同样适用于大学问的战略联盟。通过对已有的大学战略联盟的分析，提出了名校联盟与非名校联盟间的若干差异，最后提出了我国大学联盟战略的若干建议。     

Controlled linear system with delayed relay output under impulse random disturbances

The paper is devoted to the first order delayed linear system with relay output controlled by the proportional-integral (PI) regulator. The deterministic system exhibits stable oscillations, and, since the system itself is stable, it can be suitable to switch off the controller if there are no disturbances during a long time interval. In the present work, the random disturbances are modelled by a Poisson stream of impulses, and the goal is to determine the instants of switching on (off) of the PI controller. After several assumptions and quantization of the time axis, we construct the new optimal control problem which is successfully solved with the help of the dynamic programming approach.     

Heterogeneous chain propagation in low-pressure flames

An analysis of papers on hydrogen combustion at low pressures is performed, which refines the contribution of the catalytic reactions on the reactor wall to the gas-phase part of the process. A new model for the heterogeneous loss of active reaction centers was proposed and tested experimentally to explain inconsistencies that occur in some papers. In this model, the diffusion region of chain termination is formed under standard experimental conditions in vacuum oxyhydrogen flames at a reactor gas pressure a thousand times lower than the boundary pressure postulated by the previous models as the pressure below which the diffusion region of chain termination cannot be formed. __________ Translated from Fizika Goreniya i Vzryva, Vol. 42, No. 2, pp. 10–18, March–April, 2006.     

靶向抗癌药格列卫与其治疗白血病疗效监测

Gleevec是抑制致癌的融合蛋白BCR-ABL的一种分子靶向治疗药物,BCR-ABL是一种与慢性髓细胞性白血病(CML)有关的酪氨酸激酶抑制剂。用Gleevec选择性地抑制BCR-ABL活性在治疗CML中显示出很好的效果,特别是在CML慢性期,因此,监测疗效非常重要。现就Gleevec的研究进展及如何监测其在CML中的疗效作一简单综述。     

马尔可夫链在企业经济预测上的应用

为了科学的预测企业所关心的各项经济指标 ,以便为企业的未来做出正确的决策方案 ,需用适当的数学模型和方法对企业的经济活动进行定量的研究 .基于经济活动的复杂、多变性及带有许多随机性因素的特点 ,针对这两种常见的经济问题 ,分别建立了相应的马尔可夫链模型 .应用马尔可夫链的相关理论、巧妙的构造转移概率矩阵 ,只通过简单的矩阵运算 ,便迅速解决问题 .实例表明 ,马尔可夫链模型及方法在企业经济活动分析中是可行和适用的 ,可广泛用于解决企业中常见的预测及决策问题 .     

过程链管理--用于制定和管理转型组织的工具

在当今企业环境日益复杂而活跃的背景条件下，企业需要不断进行重组和迅速转型。这要求企业要具备更强的适应性和更高的灵活性。为了确保实现这些动态的要求，企业必须制定和实施新型的组织形式。本文介绍过程链管理——一种面向过程进行转型的工具。本文首先简要说明了过程链管理应用的工具、过程链管理的任务和构造。它的出发点是由行动、结构、资源、和指挥所组成的过程链管理。在第二步骤中，重点阐述了客户取向这个过程链管理中最重要的取向量。总体来说，整个价值创造链及与此相关的内部交换关系都具有重要意义。在对过程链管理的方法进行了说明以后，提出了一个应用实例，该实例不仅描述了垂直重构，也对水平重构进行了描述。     

Markov analysis of qualitative dynamics1

Common sense sometimes predicts events to be likely or unlikely rather than merely possible. We extend methods of qualitative reasoning to predict the relative likelihoods of possible qualitative behaviors by viewing the dynamics of a system as a Markov chain over its transition graph. This involves adding qualitative or quantitative estimates of transition probabilities to each of the transitions and applying the standard theory of Markov chains to distinguish persistent states from transient states and to calculate recurrence times, settling times, and probabilities for ending up in each state. Much of the analysis depends solely on qualitative estimates of transition probabilities, which follow directly from theoretical considerations and which lead to qualitative predictions about entire classes of systems. Quantitative estimates for specific systems are derived empirically and lead to qualitative and quantitative conclusions, most of which are insensitive to small perturbations in the estimated transition probabilities. The algorithms are straightforward and efficient.     

纳米SiO_2与有机物分子的亲和性和分散性

介绍纳米SiO2的合成和粒子表面的粗糙形貌以及纳米粒子链具有类似高分子链的弹性力学属性,研究纳米SiO2的亲合性,探讨亲合性对纳米SiO2的分散、改性、应用的影响。研究表明,在极性外加剂(如乙二胺、乙二醇、PEG、PDMS、PTHF、PMMA、PBMA等)存在的情况下,纳米SiO2的弹性粒子链和硅烷醇对SiO2的分散和改性具有显著的影响。带有大烷基侧链的高分子链与SiO2表面的亲合能较低,而带有官能团的高分子链与SiO2表面具有较高的亲合性。     

EM procedures using mean field-like approximations for Markov model-based image segmentation

Image segmentation using Markov random fields involves parameter estimation in hidden Markov models for which the EM algorithm is widely used. In practice, difficulties arise due to the dependence structure in the models and approximations are required. Using ideas from the mean field approximation principle, we propose a class of EM-like algorithms in which the computation reduces to dealing with systems of independent variables. Within this class, the simulated field algorithm is a new stochastic algorithm which appears to be the most promising for its good performance and speed, on synthetic and real image experiments.