André Dabrowski's work on limit theorems and weak dependence

André Robert Dabrowski, Professor of Mathematics and Dean of the Faculty of Sciences at the University of Ottawa, died October 7, 2006, after a short battle with cancer. The author of the present paper, a long‐term friend and collaborator of André Dabrowski, gives a survey of André's work on weak dependence and limit theorems in probability theory. The Canadian Journal of Statistics 37: 307–326; 2009 © 2009 Statistical Society of Canada     

Robust small area estimation

Small area estimation has received considerable attention in recent years because of growing demand for small area statistics. Basic area‐level and unit‐level models have been studied in the literature to obtain empirical best linear unbiased prediction (EBLUP) estimators of small area means. Although this classical method is useful for estimating the small area means efficiently under normality assumptions, it can be highly influenced by the presence of outliers in the data. In this article, the authors investigate the robustness properties of the classical estimators and propose a resistant method for small area estimation, which is useful for downweighting any influential observations in the data when estimating the model parameters. To estimate the mean squared errors of the robust estimators of small area means, a parametric bootstrap method is adopted here, which is applicable to models with block diagonal covariance structures. Simulations are carried out to study the behaviour of the proposed robust estimators in the presence of outliers, and these estimators are also compared to the EBLUP estimators. Performance of the bootstrap mean squared error estimator is also investigated in the simulation study. The proposed robust method is also applied to some real data to estimate crop areas for counties in Iowa, using farm‐interview data on crop areas and LANDSAT satellite data as auxiliary information. The Canadian Journal of Statistics 37: 381–399; 2009 © 2009 Statistical Society of Canada     

New aspects of Bregman divergence in regression and classification with parametric and nonparametric estimation

In statistical learning, regression and classification concern different types of the output variables, and the predictive accuracy is quantified by different loss functions. This article explores new aspects of Bregman divergence (BD), a notion which unifies nearly all of the commonly used loss functions in regression and classification. The authors investigate the duality between BD and its generating function. They further establish, under the framework of BD, asymptotic consistency and normality of parametric and nonparametric regression estimators, derive the lower bound of their asymptotic covariance matrices, and demonstrate the role that parametric and nonparametric regression estimation play in the performance of classification procedures and related machine learning techniques. These theoretical results and new numerical evidence show that the choice of loss function affects estimation procedures, whereas has an asymptotically relatively negligible impact on classification performance. Applications of BD to statistical model building and selection with non‐Gaussian responses are also illustrated. The Canadian Journal of Statistics 37: 119‐139; 2009 © 2009 Statistical Society of Canada     

Local linear fitting and improved estimation near peaks

The authors look into the problem of estimating regression functions that exhibit jump irregularities in the first derivative. They investigate the behaviour of the bias in the local linear fit and show the superior performance of appropriate one‐sided versions of the local linear fit near such irregularities. They then propose an improved estimation procedure based on data‐driven selection of a conventional or one‐sided local linear fit according to a residual sum of squares type of criterion. The authors provide theoretical results and illustrate the method both on simulated and real‐life data examples. The Canadian Journal of Statistics 37: 453–475; 2009 © 2009 Statistical Society of Canada     

基于ASP.NET的图书馆管理信息系统分析与设计

文章首先概述了图书馆自动化发展阶段、系统开发现状及存在问题;其次,介绍了ASP.NET及相关知识,包括.NET构架、ASP.NET和SQL Server 2000;以科信学院图书馆为背景,主要完成对图书馆管理信息系统的需求分析、功能模块划分和数据库模式分析,并由此设计了数据库结构和功能界面及代码,并且实现了系统的主要模块;最后,总结了本系统的特点,并提出一些尚需完善的方面.     

当代中国城乡家庭结构变动比较

 世纪," 年代末城乡核心家庭虽相对减少,然而,城乡家庭结构的简化趋势并没有改变。在城市,其标志是单人家庭上升,核心家庭中夫妇核心家庭上升,直系家庭中三代直系家庭下降。乡村三代直系家庭增加,约占乡村家庭总数的!"- 以上,达到近!" 年的最高水平;单人家庭和夫妇核心家庭则处于增长状态。当代城乡家庭结构的变动特征与二十余年来计划生育政策推行之下“少生”和“独生”环境的形成有密切关系。经济转型中人口迁移流动加速也促使家庭类型变化。在这一背景之下,家庭养老资源进一步萎缩,这对社会保障和社会服务工作提出了更迫切的要求。     

浅谈AutoCAD2000绘图新特性

文中对AutoCAD2000较AutoCADR14新增的功能进行了介绍。AutoCAD2000所具有的新特性大幅度提高了用户的设计工作效率。同时指出了AutoCAD2000存在的不足。     

基于流技术下JPEG图像在数据库中存取技术的研究与应用

在日常的人事档案管理中都会涉及到人员照片的存取,而在数据库中只能直接对BMP格式的图像进行存取,无法直接对JPEG格式图像进行存取。因此大多系统在开发时利用文件的形式来存取JPEG格式的图像。而文件的管理往往给应用系统带来不稳定的因素,介绍了利用Delphi的流技术如何实现在MS SQL Server 2000数据库中对JPEG格式图像的存取,并给出了实现的关键代码。     

Excel2000在稿件信息管理中的应用

Microsoft Excel2000是一款功能强大的电子表格软件,具有对数据进行录入、处理、分析、统计等多项功能。文章结合所在高校学报稿件管理工作的实践,介绍和探讨了Excel2000在学报稿件管理中的应用和技巧。     

A resampling approach to estimate variance components of multilevel models

In a multilevel model for complex survey data, the weight‐inflated estimators of variance components can be biased. We propose a resampling method to correct this bias. The performance of the bias corrected estimators is studied through simulations using populations generated from a simple random effects model. The simulations show that, without lowering the precision, the proposed procedure can reduce the bias of the estimators, especially for designs that are both informative and have small cluster sizes. Application of these resampling procedures to data from an artificial workplace survey provides further evidence for the empirical value of this method. The Canadian Journal of Statistics 40: 150–171; 2012 © 2012 Statistical Society of Canada