Moment adjusted imputation for multivariate measurement error data with applications to logistic regression

In clinical studies, covariates are often measured with error due to biological fluctuations, device error and other sources. Summary statistics and regression models that are based on mis-measured data will differ from the corresponding analysis based on the “true” covariate. Statistical analysis can be adjusted for measurement error, however various methods exhibit a tradeoff between convenience and performance. Moment Adjusted Imputation (MAI) is a measurement error in a scalar latent variable that is easy to implement and performs well in a variety of settings. In practice, multiple covariates may be similarly influenced by biological fluctuations, inducing correlated, multivariate measurement error. The extension of MAI to the setting of multivariate latent variables involves unique challenges. Alternative strategies are described, including a computationally feasible option that is shown to perform well.     

Comparative analysis of discretization methods in Bayesian networks

A key step in implementing Bayesian networks (BNs) is the discretization of continuous variables. There are several mathematical methods for constructing discrete distributions, the implications of which on the resulting model has not been discussed in literature. Discretization invariably results in loss of information, and both the discretization method and the number of intervals determines the level of such loss. We designed an experiment to evaluate the impact of commonly used discretization methods and number of intervals on the developed BNs. The conditional probability tables, model predictions, and management recommendations were compared and shown to be different among models. However, none of the models did uniformly well in all comparison criteria. As we cannot justify using one discretization method against others, we recommend caution when discretization is used, and a verification process that includes evaluating alternative methods to ensure that the conclusions are not an artifact of the discretization approach.     

矩原理方法及其在采矿统计分析中的应用

本文以统计分析中矩的基本概念入手,将函数逼近与线性空间的基本理论相结合,提出了—种新的统计分析方法——矩原理方法。与常规方法比较,它有着显著的优越性。本文进行了详细的理论推导和算例检验,同时指出了该方法在采矿统计分析中的应用。     

正弦波纹板第二主刚度的补正

由各向同性材料构成的构造上各向异性板,如正弦波纹板,当板厚达到一定数值时,Seydel的第二主向刚度公式不再适用。依据平板的抗弯刚度公式,提出等效惯性矩的思想,构造适应较厚波纹板的第二主刚度公式。通过对四边简支的波纹板的计算和实验测量,证明在板较薄时Seydel的公式较适用,而当板厚尺寸增加到一定值时,文中给出的第二主刚度公式接近实验值。     

Variance reduction for fixed-source Monte Carlo calculations in multiplying systems by improving chain-length statistics

For a fixed-source calculation in a multiplying system, the variance in the neutron-chain length can create an unstable calculation and a neutron flux with high variance. In this paper an analytical frame is derived to calculate a priori the variance of the neutron flux in a simple model of a fixed-source calculation. This method is then used to find better variance reduction techniques not only for this simplified problem, but also for general cases.     

Design equations for moment strength of RBS-B connections

Reduced Beam Section (RBS) moment connections are newly developed connections for Special Moment Resisting Frames (SMRF). This study focuses on Reduced Beam Section with Bolted web connections (RBS-B). Several experiments have demonstrated that some RBS-B connection specimens failed prior to reaching the plastic moment capacity of the reduced beam sections: this may be attributed to insufficient moment strength in the RBS-B connections. This study proposes a new procedure for accurately computing the moment strength for ensuring that a plastic hinge forms in the reduced-section region of the beam, as intended by design.     

减速器的最佳传动比分配

速比分配与传动齿轮转动惯量推算有密切关系，但按转动惯量为极小值推导而得的最佳速比公式又不直全面执行，而应按不同情况进行速比分配．     

继电器运动系统转动惯量计算通用软件

继电器动态特性研究关键问题之一是转动惯量的正确计算。本文用直角网格的数值解法，计算复杂几何形状物体的转动惯量，包括数学模型、求解原理、程序框图、计算实例。     

钢筋砼连续梁优化设计的弯矩分配法

本文首先给出了用各跨最大变矩、最大剪力或跨度表达的单跨钢筋砼梁最优刚度，然后基于分部优化法提出了钢筋砼连续梁优化设计的弯矩分配法，对节点的不平衡弯矩按内力或跨度表达的最优刚度进行一次弯矩分配，手算和电算都很简捷方便。