Using auxiliary data for parameter estimation with non-ignorably missing outcomes

We propose a method for estimating parameters in generalized linear models when the outcome variable is missing for some subjects and the missing data mechanism is non-ignorable. We assume throughout that the covariates are fully observed. One possible method for estimating the parameters is maximum likelihood with a non-ignorable missing data model. However, caution must be used when fitting non-ignorable missing data models because certain parameters may be inestimable for some models. Instead of fitting a non-ignorable model, we propose the use of auxiliary information in a likelihood approach to reduce the bias, without having to specify a non-ignorable model. The method is applied to a mental health study.     

Empirical Likelihood for Censored Linear Regression

In this paper we investigate the empirical likelihood method in a linear regression model when the observations are subject to random censoring. An empirical likelihood ratio for the slope parameter vector is defined and it is shown that its limiting distribution is a weighted sum of independent chi-square distributions. This reduces to the empirical likelihood to the linear regression model first studied by Owen (1991) if there is no censoring present. Some simulation studies are presented to compare the empirical likelihood method with the normal approximation based method proposed in Lai et al. (1995). It was found that the empirical likelihood method performs much better than the normal approximation method.     

A simple method for estimating a regression model for κ between a pair of raters

Agreement studies commonly occur in medical research, for example, in the review of X-rays by radiologists, blood tests by a panel of pathologists and the evaluation of psychopathology by a panel of raters. In these studies, often two observers rate the same subject for some characteristic with a discrete number of levels. The κ-coefficient is a popular measure of agreement between the two raters. The κ-coefficient may depend on covariates, i.e. characteristics of the raters and/or the subjects being rated. Our research was motivated by two agreement problems. The first is a study of agreement between a pastor and a co-ordinator of Christian education on whether they feel that the congregation puts enough emphasis on encouraging members to work for social justice (yes versus no). We wish to model the κ-coefficient as a function of covariates such as political orientation (liberal versus conservative) of the pastor and co-ordinator. The second example is a spousal education study, in which we wish to model the κ-coefficient as a function of covariates such as the highest degree of the father of the wife and the father of the husband. We propose a simple method to estimate the regression model for the κ-coefficient, which consists of two logistic (or multinomial logistic) regressions and one linear regression for binary data. The estimates can be easily obtained in any generalized linear model software program.     

On repeated measures analysis with misspecified covariance structure

In recent years various sophisticated methods have been developed for the analysis of repeated measures, or longitudinal data. The more traditional approach, based on a normal likelihood function, has been shown to be unsatisfactory, in the sense of yielding asymptotically biased estimates when the covariance structure is misspecified. More recent methodology, based on generalized linear models and quasi-likelihood estimation, has gained widespread acceptance as 'generalized estimating equations'. However, this also has theoretical problems. In this paper a suggestion is made for improving the asymptotic behaviour of estimators by using the older approach, implemented via Gaussian estimation. The resulting estimating equations include the quasi-score function as one component, so the methodology proposed can be viewed as a combination of Gaussian estimation and generalized estimating equations which has a firmer asymptotic basis than either alone has.     

Estimation and selection procedures in regression: An L1 approach

The authors consider the problem of estimating a regression function go involving several variables by the closest functional element of a prescribed class _G that is closest to it in the L₁ norm. They propose a new estimator ? based on independent observations and give explicit finite sample bounds for the L₁distance between ?g and go. They apply their estimation procedure to the problem of selecting the smoothing parameter in nonparametric regression.     

Statistical analyses for round robin interaction data

This paper considers the analysis of round robin interaction data whereby individuals from a group of subjects interact with one another, producing a pair of outcomes, one for each individual. The authors provide an overview of the various analyses applied to these types of data and extend the work in several directions. In particular, they provide a fully Bayesian analysis for such data and use a real data example for illustration purposes.     

线性度对线性调频雷达距离分辨力的影响

讨论了线性度对线性调频雷达距离分辨力的影响,求得了线性调频雷达的极限分辨力,导出了调频非线性对距离分辨力影响的数学表达式,以及用于工程的近似估计公式。用计算机仿真验证了这种影响。仿真结果和理论分析表明,距离分辨力随以下三个参数的增大而降低:国标距离,频率偏离函数F(t)的高次谐波和最大值。线性调频雷达的极限分辨力取决于发射波形的带宽。     

关于Klein二次超曲面Q_2~4极超平面的一个注记

本文主要是把三维射影空间P~3中的直线坐标看作五维射影空间P~5中的点坐标,这祥P~3中的直线与P~5中的点可建立对应关系,并研究了点在P~6中的直线、曲线、平面和曲面上变动时,它关于Klein二次超曲面Q_2~4的配极超平面的包含关系.     

常系数线性常微分方程及欧拉方程通解的残数表示式

本文应用残数理论建立了 n 阶常系数线性微分方程及欧拉方程通解的另一种表示形式.n 阶非齐次常系数线性微分方程通解的表达式为函数f(z')·e~x/g(z)与F(t)dt/g(z)在极点zj(j=l,2,…l)的残数之和。其中g(x)是z 的n次多项式,在z_j (j=1,2,…l)的值为零,f(z)是任一个解析函数,=1,2,…l)的值不为零.欧拉方程通解有类似结果.     

陕西省农村居民消费结构的计量分析

消费需求是拉动经济增长的根本动因。运用扩展线性支出系统模型对陕西省农村居民的边际消费倾向、收入弹性和价格弹性进行实证分析，结果显示：陕西省农村居民仍然处于生存型消费阶段，消费支出主要集中在食品、教育和居住方面。要提高农村居民消费水平，必须千方百计增加农民收入，普及农村教育，改善农村消费环境。