A robust approach to longitudinal data analysis

The author introduces robust techniques for estimation, inference and variable selection in the analysis of longitudinal data. She first addresses the problem of the robust estimation of the regression and nuisance parameters, for which she derives the asymptotic distribution. She uses weighted estimating equations to build robust quasi‐likelihood functions. These functions are then used to construct a class of test statistics for variable selection. She derives the limiting distribution of these tests and shows its robustness properties in terms of stability of the asymptotic level and power under contamination. An application to a real data set allows her to illustrate the benefits of a robust analysis.     

ON SELECTING VARIABLES AND ASSESSING THEIR PERFORMANCE IN LINEAR DISCRIMINANT ANALYSIS

Linear discriminant analysis between two populations is considered in this paper. Error rate is reviewed as a criterion for selection of variables, and a stepwise procedure is outlined that selects variables on the basis of empirical estimates of error. Problems with assessment of the selected variables are highlighted. A leave-one-out method is proposed for estimating the true error rate of the selected variables, or alternatively of the selection procedure itself. Monte Carlo simulations, of multivariate binary as well as multivariate normal data, demonstrate the feasibility of the proposed method and indicate its much greater accuracy relative to that of other available methods.     

A bias analysis of Weibull models under heaped data

Retrospectively collected duration data are often reported incorrectly. An important type of such an error is heaping—respondents tend to round-off or round-up the data according to some rule of thumb. For two special cases of the Weibull model we study the behaviour of the ‘naive estimators’, which simply ignore the measurement error due to heaping, and derive closed expressions for the asymptotic bias. These results give a formal justification of empirical evidence and simulation-based findings reported in the literature. Additionally, situations where a remarkable bias has to be expected can be identified, and an exact bias correction can be performed.     

Slack-variable models versus Scheffé's mixture models

Slack-variable models are compared against Scheffé's polynomial model for mixture experiments. The notion of model equivalence and the use of various diagnostic measures provide effective tools in making such comparisons, particularly when the experimental region is highly constrained. It is demonstrated that the choice of the best fitting model, through variable selection, depends on which mixture component is selected as a slack variable, and on the size of the fitted model. In addition, the equivalence of two well-known representations of a complete mixture model is shown to be valid. Two numerical examples are presented.     

关于齐次马氏信源二元函数的一类偏差定理

利用任意信源相对熵密度偏差的概念,使用分析的方法研究了齐次马氏信源的一类强偏差定理,得出了一个二元函数的一类强偏差定理和几个极限性质.     

可编程控制器在洁净空调自动控制系统中的应用

介绍了可编程控制器(PLC)在洁净空调控制系统中应用的实例，对应用中遇到的问题提出了解决方案，并对PLC在洁净空调系统中推广使用的前景进行了分析。     

未知相对度系统的一阶D型迭代学习控制

针对未知相对度系统,提出了一种一阶D型迭代学习控制律的设计方法。通过对具有未知相对度的被控系统串连和并联一个一阶子系统,可构造一个相对度为1的虚拟系统。该虚拟系统在一阶D型迭代学习控制律的作用下,能够完全跟踪期望轨迹。从而实现被控系统在一定误差范围内给定轨迹的渐近跟踪,D型迭代学习控制律的增益不依赖于被控系统的参数。仿真实例验证了方法的可行性与有效性。     

关于Bellman問題

本文首先研究了常时滞线性方程与变时滞线性方程解之间的一致性问题,然后作为特殊情况,解决了Bellman于1965年提出的一个问题。     

一种瞬变步长LMS自适应滤波算法

基于规一化起动、平滑递推和ＰＬＳ算法导出了一种新的变步长ＬＭＳ算法。文中分析了起动后的平稳连接和提高起始收敛速度的条件，证明了步长因子μ（ｋ）的瞬变特性及其对减小失调噪声的作用。计算机仿真效果较好，支持了理论分析。     

变系数线性大系统稳定性的M-判别法(英文)

本文沿用文［1］的思想方法，以M矩阵为工具，研究变系数线性大系统零解的稳定性，得到一系列稳定性的显式代数判据，我们统称为M－判别法。