Evaluation of an analysis approach used to account for extra-variation in clustered categorical responses

This article presents the results of a simulation study investigating the performance of an approach developed by Miller and Landis (1991) for the analysis of clustered categorical responses. Evaluation of this “two-step” approach, which utilizes the method of moments to estimate the extra-variation pardmeters and subsequently incorporates these parameters into estimating equations for modelling the marginal expectations, is carried out in an experimental setting involving a comparison between two groups of observations. We assume that data for both groups are collected from each cluster and responses are measured on a three-point ordinal scale. The performance of the estimators used in both “steps” of the analysisis investigated and comparisons are made to an alternative analysismethod that ignores the clustering. The results indicate that in the chosen setting the test for a difference between groups generally operatbs at the nominal α=0.05 for 10 or more clusters and hasincreasing power with both an increasing number of clusters and an inrreasing treatment effect. These results provide a striking contrasc to those obtained from an improper analysis that ignores clustering.     

Estimable functions in the nonlinear model

When the method of least squares is used to estimate the parameters in a general model and the generated system of normal equations is linearly dependent, the estimate of the vector of parameters which satisfies the criterion is not unique. However, there exist certain functions of the estimated vector of parameters which are invariant to the least squares solution obtained from the normal equations. We define those invariant functions to be estimable, and present a technique to determine the functions of the parameters which are estimable for the general model. The method results in solving either a linear first order partial differential equation or a system of linear first order partial differential equations corresponding, respectively, to a single or multiple dependency between columns of the Jacobian matrix of the mean of the model. The usual results concerning estimability for linear models are a special case of the general results developed.     

Nonparametric estimating equations based on a penalized information criterion

It has recently been observed that, given the mean‐variance relation, one can improve on the accuracy of the quasi‐likelihood estimator by the adaptive estimator based on the estimation of the higher moments. The estimation of such moments is usually unstable, however, and consequently only for large samples does the improvement become evident. The author proposes a nonparametric estimating equation that does not depend on the estimation of such moments, but instead on the penalized minimization of asymptotic variance. His method provides a strong improvement over the quasi‐likelihood estimator and the adaptive estimators, for a wide range of sample sizes.     

一类一阶线性方程组的解法

利用复系数的一阶线性方程解公式，给出一类一阶线性方程组的新解性。     

Simplified Estimating Functions for Diffusion Models with a High-dimensional Parameter

We consider estimating functions for discretely observed diffusion processes of the following type: for one part of the parameter of interest we propose to use a simple and explicit estimating function of the type studied by Kessler (2000); for the remaining part of the parameter we use a martingale estimating function. Such an approach is particularly useful in practical applications when the parameter is high-dimensional. It is also often necessary to supplement a simple estimating function by another type of estimating function because only the part of the parameter on which the invariant measure depends can be estimated by a simple estimating function. Under regularity conditions the resulting estimators are consistent and asymptotically normal. Several examples are considered in order to demonstrate the idea of the estimating procedure. The method is applied to two data sets comprising wind velocities and stock prices. In one example we also propose a general method for constructing diffusion models with a prescribed marginal distribution which have a flexible dependence structure.     

线性抛物型方程的窄四边形元逼近方法

讨论了一类发展方程——线性抛物型方程的窄四边形元逼近方法,导出了相应的半离散格式和全离散格式,并通过一些新的技巧,得到了相应的误差估计.     

我国A股上市公司红利政策影响因素分析

红利分配政策是上市公司研究的重要方面。文章利用A股全部非金融类上市公司财务数据,通过统计分析了1992-2004财务期间A股市场分红状况和特点,并使用统计分析和LOGIT回归两种方法,分析了这些年间A股公司红利政策的影响因素。研究发现,盈利能力、公司规模、股权融资是红利政策的显著影响因素,但投资机会对红利政策影响不显著。     

T-optimum designs for discrimination between two multiresponse dynamic models

Summary.  The paper is concerned with a problem of finding an optimum experimental design for discriminating between two rival multiresponse models. The criterion of optimality that we use is based on the sum of squares of deviations between the models and picks up the design points for which the divergence is maximum. An important part of our criterion is an additional vector of experimental conditions, which may affect the design. We give the necessary conditions for the design and the additional parameters of the experiment to be optimum, we present the algorithm for the numerical optimization procedure and we show the relevance of these methods to dynamic systems, especially to chemical kinetic models.     

平面电磁波在两种不同媒质中传播特性的比较

从麦克斯韦方程出发,对平面电磁波在两种不同媒质中的传播进行了具体的分析、比较,说明电磁波的传播与媒质有很大关系。     

多项式、线性方程组及范德蒙行列式的相互应用

本文试图探讨多项式、线性方程组、范德蒙行列式的相互应用,从而揭示了它们的相互关系.