DELPHI中图形的连续旋转与动画的实现

阐述了在DELPHI语言中利用Canvas的Pixels[]属性来实现图形连续旋转和利用CopyRect()函数实现动画的方法 .     

Estimation of generalized linear latent variable models

Summary.  Generalized linear latent variable models (GLLVMs), as defined by Bartholomew and Knott, enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the complexity of the log-likelihood function of a GLLVM, an approximation such as numerical integration must be used for inference. This can limit drastically the number of variables in the model and can lead to biased estimators. We propose a new estimator for the parameters of a GLLVM, based on a Laplace approximation to the likelihood function and which can be computed even for models with a large number of variables. The new estimator can be viewed as an M -estimator, leading to readily available asymptotic properties and correct inference. A simulation study shows its excellent finite sample properties, in particular when compared with a well-established approach such as LISREL. A real data example on the measurement of wealth for the computation of multidimensional inequality is analysed to highlight the importance of the methodology.     

几类矩阵的研究及其应用

对几类矩阵进行了刻划；对于非负阵谱半径的一个重要性质￣［１］，给出了新的简单证明；作为应用，得到有重要实际意义的某些类矩阵之逆的谱半径的界的估计。     

用牛顿环实验测量凸透镜曲率半径的误差分析

对牛顿环测量凸透镜曲率半径实验中的误差进行了理论分析和半定量分析,指出了误差的主要来源及相应的处理方法.对于测量曲率半径P>lm的平凸透镜,公式R=Dm2-Dn2/4（m-n）λ的理论相对误差小于10-8;逐差法可以消除形变带来的系统误差;由于形变的影响应从第5级以后开始计数,对于R>1m的凸透镜要得到小于10-3的相对误差,级数应小于50,级数差值应大于30.钠光灯作为光源的系统相对误差为7.1×10-4.     

一类边值问题临界解的扰动性质

在LP(1 p <+∞ ) 空间中证明了一类非齐次积一秩微分方程边值问题的临界解关于方程系数和区域尺寸的连续依赖性 ,并获得了相应的高阶扰动公式     

三个两两相外切圆的内公切圆与外公切圆

在适当的直角坐标系下求出了三个两两相外切圆的内公切圆与外公切圆之圆心坐标和半径,并得到了诸半径之间的优美关系式。     

光纤转角、位移传感器的理论研究

提出了“光纤桥”新概念,利用“光纤桥”构成了测量转角、位移的光纤传感器。利用准高斯光束假设,详细地分析、计算了系统参数对传感器性能的影响,理论上预见了测角精度可达1”,测角范围±6′。     

块Jacobi迭代阵的收敛性

研究大型线性方程组迭代解法中分块ＪＡＣＯＢＩ迭代阵的收敛性。采用块矩阵分析方法和谱半径降维估计法得到块Ｊａｃｏｂｉ迭代阵收敛的实用充分条件。     

SIMPCA: a framework for rotating and sparsifying principal components

We propose an algorithmic framework for computing sparse components from rotated principal components. This methodology, called SIMPCA, is useful to replace the unreliable practice of ignoring small coefficients of rotated components when interpreting them. The algorithm computes genuinely sparse components by projecting rotated principal components onto subsets of variables. The so simplified components are highly correlated with the corresponding components. By choosing different simplification strategies different sparse solutions can be obtained which can be used to compare alternative interpretations of the principal components. We give some examples of how effective simplified solutions can be achieved with SIMPCA using some publicly available data sets.     

A family of estimators of population variance in two-occasion rotation patterns

Abstract

In this article, we have considered the problem of estimation of population variance on current (second) occasion in two occasion successive (rotation) sampling. A class of estimators of population variance has been proposed and its asymptotic properties have been discussed. The proposed class of estimators is compared with the sample variance estimator when there is no matching from the previous occasion and the Singh et al. (2013) estimator. Optimum replacement policy is discussed. It has been shown that the suggested estimator is more efficient than the Singh et al. (2013) estimator and a usual unbiased estimator when there is no matching. An empirical study is carried out in support of the present study.