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.     

中国家电制造企业渠道建设研究

针对中国家电制造企业渠道建设问题,阐述了四种主要的营销渠道:区域多家代理制、区域总代理制、大终端直销模式和控制渠道的自营模式,从渠道多元化、渠道整合和拓展新型营销渠道三方面提出了家电制造企业的营销渠道建设对策.并进一步分析了家电制造企业四种主要的物流渠道:自营物流为主,外包物流为辅;成立独立的物流子公司;与物流企业合资成立物流公司和以外包为主.在此基础上,从建立战略联盟,构筑柔性的物流渠道和配送共同化三方面提出了深化家电制造企业物流渠道的对策.     

有多个销售渠道的连续时间收益管理问题研究

研究了有多个销售渠道的连续时间收益管理问题.利用最优控制理论,建立了最大期望收益函数所满足的HJB最优方程,证明了最大期望收益是剩余商品数量的递增凹函数、是销售时间的递减凹函数,最优定价策略随剩余商品数量及销售时间递减;得到了不同销售渠道的最优定价之间所满足的一个关系式.最后,利用数值仿真的方法对不同销售渠道差别定价的效果进行了讨论.     

The cost of not knowing the radius

Robust Statistics considers the quality of statistical decisions in the presence of deviations from the ideal model, where deviations are modelled by neighborhoods of a certain size about the ideal model. We introduce a new concept of optimality (radius-minimaxity) if this size or radius is not precisely known: for this notion, we determine the increase of the maximum risk over the minimax risk in the case that the optimally robust estimator for the false neighborhood radius is used. The maximum increase of the relative risk is minimized in the case that the radius is known only to belong to some interval [r _l ,r _u ]. We pursue this minmax approach for a number of ideal models and a variety of neighborhoods. Also, the effect of increasing parameter dimension is studied for these models. The minimax increase of relative risk in case the radius is completely unknown, compared with that of the most robust procedure, is 18.1% versus 57.1% and 50.5% versus 172.1% for one-dimensional location and scale, respectively, and less than 1/3 in other typical contamination models. In most models considered so far, the radius needs to be specified only up to a factor , in order to keep the increase of relative risk below 12.5%, provided that the radius–minimax robust estimator is employed. The least favorable radii leading to the radius–minimax estimators turn out small: 5–6% contamination, at sample size 100.      

On the equivalence of three estimators for dispersion effects in unreplicated two-level factorial designs

Box and Meyer [1986. Dispersion effects from fractional designs. Technometrics 28(1), 19–27] were the first to consider identifying both location and dispersion effects from unreplicated two-level fractional factorial designs. Since the publication of their paper a number of different procedures (both iterative and non-iterative) have been proposed for estimating the location and dispersion effects. An overview and a critical analysis of most of these procedures is given by Brenneman and Nair [2001. Methods for identifying dispersion effects in unreplicated factorial experiments: a critical analysis and proposed strategies. Technometrics 43(4), 388–405]. Under a linear structure for the dispersion effects, non-iterative estimation methods for the dispersion effects were proposed by Brenneman and Nair [2001. Methods for identifying dispersion effects in unreplicated factorial experiments: a critical analysis and proposed strategies. Technometrics 43(4), 388–405], Liao and Iyer [2000. Optimal 2^n-p$2^{n-p}$ fractional factorial designs for dispersion effects under a location-dispersion model. Comm. Statist. Theory Methods 29(4), 823–835] and Wiklander [1998. A comparison of two estimators of dispersion effects. Comm. Statist. Theory Methods 27(4), 905–923] (see also Wiklander and Holm [2003. Dispersion effects in unreplicated factorial designs. Appl. Stochastic. Models Bus. Ind. 19(1), 13–30]). We prove that for two-level factorial designs the proposed estimators are different representations of a single estimator. The proof uses the framework of Seely [1970a. Linear spaces and unbiased estimation. Ann. Math. Statist. 41, 1725–1734], in which quadratic estimators are expressed as inner products of symmetric matrices.     

连锁经营物流配送系统的动态分析模型研究

本文提出了研究连锁经营零售商业的物流配送总体计划的大型线性目标规划数学模型,并用动态目标规划的方法来模拟和动态地分析配送系统运行性能和合理性。详细论述了模型结构,并以大型问题的实例进行了测试,证实了方法的正确性和可用性。     

电视频道主持人品牌化建构的基础与路径——以CCTV-2财经频道为例

这是一个品牌消费和品牌竞争的时代,品牌主持人已成为各个电视频道的重要战略资源。本文基于对CCTV-2财经频道打造品牌主持人实践的解析,提炼了主持人品牌化建构基础和实施路径两个方面的思路。主持人品牌化建构的基础有三个,一是文化自觉而产生的人格魅力,二是作为主持人专业主义体现的话语技术,三是主持人的情绪场控制能力。主持人品牌化建构的实施路径体现在品牌规划上的开拓创新意识,品牌运营的分级分类管理和品牌营销的系统化品牌包装等方面。     

对称不定线性系统的不定预处理技术

研究求解对称不定线性系统Ax=b的不定不完全分解预处理算法,其中A为稀疏的对称不定矩阵。合适的选主元算法是成功分解不定矩阵的关键,为了加快选主元的速度,给出了松弛的有界Bunch-Kaufman(RBBK)对称选主元算法,并分析了该选主元算法的稳定性以及参数的选择范围。将RBBK算法与不完全Cholesky分解相结合,得到了一类稳定性较高的修改的不完全Cholesky分解预处理技术。MATLAB下的数值例子表明,将提出的预处理技术用于SQMR迭代算法时,得到较快的收敛速度。     

Two kinds of restricted modified estimators in linear regression model

In this paper, we introduce two kinds of new restricted estimators called restricted modified Liu estimator and restricted modified ridge estimator based on prior information for the vector of parameters in a linear regression model with linear restrictions. Furthermore, the performance of the proposed estimators in mean squares error matrix sense is derived and compared. Finally, a numerical example and a Monte Carlo simulation are given to illustrate some of the theoretical results.     

Generalized Empirical Likelihood Inference in Generalized Linear Models for Longitudinal Data

In this article, the generalized linear model for longitudinal data is studied. A generalized empirical likelihood method is proposed by combining generalized estimating equations and quadratic inference functions based on the working correlation matrix. It is proved that the proposed generalized empirical likelihood ratios are asymptotically chi-squared under some suitable conditions, and hence it can be used to construct the confidence regions of the parameters. In addition, the maximum empirical likelihood estimates of parameters are obtained, and their asymptotic normalities are proved. Some simulations are undertaken to compare the generalized empirical likelihood and normal approximation-based method in terms of coverage accuracies and average areas/lengths of confidence regions/intervals. An example of a real data is used for illustrating our methods.