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.     

低压直流供电电路中高压直流的产生

在由低压直流电源供电的电路中,往往电路的某些部位需要使用高于电源所供的电压,将电源所供的低压转换为较高的电压,常用的方法有3种:自举升压、电感升压、逆变升压。结合实际应用电路对各种升压方法从器件选择、升压原理、升压结果等诸方面进行分析、探讨,解决了总体供电电压不变时,局部高压产生的关键性问题。它对电子电器电路的设计人员有一定的启发作用,对优化电路、遴选器件有较强的参考价值。     

Identification of the minimum effective dose for right-censored survival data

In this paper, we consider identifying the minimum effective dose (MED) in a dose-response study when survival data are subject to random right-censorship, where the MED is defined to be the smallest dose level under study that has survival advantage over the zero-dose control. To this end, we suggest single-step-down testing procedures based on three different types of weighted logrank statistics, respectively. The comparative results of a Monte Carlo error rate and power/bias study for a variety of survival and censoring distributions are then presented and discussed. The application of the testing procedures for identifying the MED is finally illustrated by using a numerical example of prostate cancer data.     

Studies on adsorption equilibrium of xylenes in AEL framework using biased GCMC and energy minimization

The adsorption of xylene isomers in AlPO₄-11 (AEL network) was investigated using biased grand canonical Monte Carlo (GCMC) simulations. Preferential o-xylene adsorption was predicted by the simulations, in agreement with previously reported experimental data. In AlPO₄-11 the selective adsorption behavior comes from the smaller length of the o-xylene molecule along the crystallographic c-axis compared to p-xylene. This is in contrast to AlPO₄-5 and AlPO₄-8, where the ortho-selectivity is caused by the characteristic face-to-face positioning of o-xylene. Energy minimization studies were also performed in a flexible AlPO₄-11 lattice to study the structural changes upon xylene adsorption. The energy minimization study showed that the AlPO₄-11 crystal distorts upon p- and o-xylene adsorption. The distortion mechanism is related to the strong interaction between xylene methyl groups and the sieve oxygen atoms in the O3 position in the wide region of the pore.     

铰孔孔径增大的工艺探析

通过对铰孔孔径增大，误差大产生原因的工艺探讨、分析，提出了有效、切实可行的避免措施，通过灵活应用，提高了产品质量。     

水系统的积垢及其物理控制技术

主要综述了水系统中水垢的种类、形成机理及其物理防治方法。详细阐述了静电、电子以及磁化等物理控垢法的工作原理及技术要点,并指出了其发展方向。     

SOC设计中的浮动压焊块电源/地线网络设计和优化

随着VLSI工艺技术的发展和芯片规模的不断增加，尤其是在SOC设计中，原有的那种供电压焊块只能位于芯片边缘的确定益的模式已经不能够满足整个电路性能的需要。在很多情况下，依靠在电源线的拓扑结构确定后的线宽优化，还是无法保证在有限的布线资源下为电路提供可靠的高性能的供电需求。由此，出现了在芯片边缘上浮动放置压焊块及在芯片的顶部放置供电压焊块阵列的方法。文中提出了一种用于SOC设计中新的基于树型结构的浮动压焊块的电源／地线网络优化算法，经过MCNC电路实例测试后得到明显的优化结果。     

Analysis of the Datagram Congestion Control Protocol’s connection management procedures using the sweep-line method

State space explosion is a key problem in the analysis of finite state systems. The sweep-line method is a state exploration method which uses a notion of progress to allow states to be deleted from memory when they are no longer required. This reduces the peak number of states that need to be stored, while still exploring the full state space. The technique shows promise but has never achieved reductions greater than about a factor of 10 in the number of states stored in memory for industrially relevant examples. This paper discusses sweep-line analysis of the connection management procedures of a new Internet standard, the Datagram Congestion Control Protocol (DCCP). As the intuitive approaches to sweep-line analysis are not effective, we introduce new variables to track progress. This creates further state explosion. However, when used with the sweep-line, the peak number of states is reduced by over two orders of magnitude compared with the original. Importantly, this allows DCCP to be analysed for larger parameter values. Somsak Vanit-Anunchai was partially supported by an Australian Research Council Discovery Grant (DP0559927) and Suranaree University of Technology. Guy Edward Gallasch was supported by an Australian Research Council Discovery Grant (DP0559927).     

On the binomial confidence interval and probabilistic robust control

The Clopper-Pearson confidence interval has ever been documented as an exact approach in some statistics literature. More recently, such approach of interval estimation has been introduced to probabilistic control theory and has been referred as non-conservative in control community. In this note, we clarify the fact that the so-called exact approach is actually conservative. In particular, we derive analytic results demonstrating the extent of conservatism in the context of probabilistic robustness analysis. This investigation encourages seeking better methods of confidence interval construction for robust control purpose.