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.     

Anti-Dissipative Schemes for Advection and Application to Hamilton–Jacobi–Bellmann Equations

We propose two new antidiffusive schemes for advection (or linear transport), one of them being a mixture of Roe’s Super-Bee scheme and of the “Ultra-Bee” scheme. We show how to apply these schemes to treat time-dependent first order Hamilton–Jacobi–Bellman equations with discontinuous initial data, possibly infinitely-valued. Numerical tests are proposed, in one and two space dimensions, in order to validate the methods AMS subject classifications. Primary 65M12, Secondary 58J47     

基于随机分析和概率预测的固定式海洋平台结构风险评估研究

对极端海况进行了环境荷载的联合概率分析。引入了整体不确定性和敏感性分析理论,利用非正态过程、具有不同相关性的多变量随机模拟技术,求解结构的失效概率。提出结构失效分析的新方法,并利用DNV提供的历史数据,对结构失效的后果做了适当分析。以埕北12-C井组平台为实例,对平台甲板高程(Air Gap)失效分析进行了实例计算。结果表明,使用多维联合极值分布理论及相应的求解方法,是离岸工程结构物风险评估的重要手段。     

Optimal and self‐tuning fusion Kalman filters for discrete‐time stochastic singular systems

Based on the optimal fusion estimation algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion Kalman filter weighted by scalars is presented for discrete‐time stochastic singular systems with multiple sensors and correlated noises. A cross‐covariance matrix of filtering errors between any two sensors is derived. When the noise statistical information is unknown, a distributed identification approach is presented based on correlation functions and the weighted average method. Further, a distributed self‐tuning fusion filter is given, which includes two stage fusions where the first‐stage fusion is used to identify the noise covariance and the second‐stage fusion is used to obtain the fusion state filter. A simulation verifies the effectiveness of the proposed algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

Multiplicative cases from additive cases: Extension of Kolmogorov–Feller equation to parametric Poisson white noise processes

In this paper the response of nonlinear systems driven by parametric Poissonian white noise is examined.As is well known, the response sample function or the response statistics of a system driven by external white noise processes is completely defined. Starting from the system driven by external white noise processes, when an invertible nonlinear transformation is applied, the transformed system in the new state variable is driven by a parametric type excitation. So this latter artificial system may be used as a tool to find out the proper solution to solve systems driven by parametric white noises. In fact, solving this new system, being the nonlinear transformation invertible, we must pass from the solution of the artificial system (driven by parametric noise) to that of the original one (driven by external noise, that is known). Moreover, introducing this invertible nonlinear transformation into the Itô’s rule for the original system driven by external input, one can derive the Itô’s rule for systems driven by a parametric type excitation, directly. In this latter case one can see how natural is the presence of the Wong–Zakai correction term or the presence of the hierarchy of correction terms in the case of normal and Poissonian white noise, respectively. Direct transformation on the Fokker–Planck and on the Kolmogorov–Feller equation for the case of parametric input are found.     

一种外置式视频同步信号发生器的设计

对随机视频信号的特点进行了研究，提出了采用谱分析提取随机视频信号的同步频率的方法，根据数据传输的要求设计了基于USB接口的外置式视频同步信号发生器并给出了系统的硬件框图和软件流程。系统具有自动分析随机视频信息隐含的同步信号频率，精确产生同步脉冲信号的特点，满足随机视频信号采集和稳定显示的同步要求。同步精度达到300帧内图像漂移小于1个像素。利用该视频同步信号发生器控制非标准图像采集卡实现了计算机辐射的随机视频信号的采集和稳定显示。     

The Transport Properties of Hot Electrons in a Xenon/Methane Mixture

The results are given of numerical solution of the Boltzmann equation in a binomial approximation in view of elastic and inelastic electron collisions in Xe+CH₄ mixtures and in pure methane. The electron energy distribution functions obtained are used to calculate the electron transport coefficients for E/N values of up to several Townsends, i.e., the drift velocity, mobility, average and characteristic energies, diffusion coefficient. The results of calculation for pure methane fit the available experimental data. A similarity rule is found for the electron transport coefficients in a Xe+CH₄ mixture with different concentrations of methane molecules, which enables one to determine the values of transport coefficients in a mixture with a minor (less than 30 percents) methane content.     

On the Consistency of the Normalized Eight-Point Algorithm

A recently proposed argument to explain the improved performance of the eight-point algorithm that results from using normalized data (Chojnacki, W., et al. in IEEE Trans. Pattern Anal. Mach. Intell. 25(9):1172–1177, 2003) relies upon adoption of a certain model for statistical data distribution. Under this model, the cost function that underlies the algorithm operating on the normalized data is statistically more advantageous than the cost function that underpins the algorithm using unnormalized data. Here we extend this explanation by introducing a more refined, structured model for data distribution. Under the extended model, the normalized eight-point algorithm turns out to be approximately consistent in a statistical sense. The proposed extension provides a link between the existing statistical rationalization of the normalized eight-point algorithm and the approach of Mühlich and Mester for enhancing total least squares estimation methods via equilibration. The paper forms part of a wider effort to rationalize and interrelate foundational methods in vision parameter estimation.     

求非线性方程近似解的逐次迭代法

本文提出了求解非线性问题的一种新方法——逐次迭代法，本方法先是给出一个初始近似解，然后将这个近似解进行校正迭代，使之接近于真实解，把一个非线性问题转化为一个线性问题来解决。