Examples of the use of an equivalence theorem in constructing optimum experimental designs for random-effects nonlinear regression models

The problem considered is that of finding an optimum measurement schedule to estimate population parameters in a nonlinear model when the patient effects are random. The paper presents examples of the use of sensitivity functions, derived from the General Equivalence Theorem for D-optimality, in the construction of optimum population designs for such schedules. With independent observations, the theorem applies to the potential inclusion of a single observation. However, in population designs the observations are correlated and the theorem applies to the inclusion of an additional measurement schedule. In one example, three groups of patients of differing size are subject to distinct schedules. Numerical, as opposed to analytical, calculation of the sensitivity function is advocated. The required covariances of the observations are found by simulation.     

Non-ignorable missing covariate data in survival analysis: a case-study of an International Breast Cancer Study Group trial

Summary.  Non-ignorable missing data, a serious problem in both clinical trials and observational studies, can lead to biased inferences. Quality-of-life measures have become increasingly popular in clinical trials. However, these measures are often incompletely observed, and investigators may suspect that missing quality-of-life data are likely to be non-ignorable. Although several recent references have addressed missing covariates in survival analysis, they all required the assumption that missingness is at random or that all covariates are discrete. We present a method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non-ignorable and continuous or discrete. Our method is useful in reducing the bias and improving efficiency in the presence of missing data. The methodology clearly specifies assumptions about the missing data mechanism and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results.     

Statistical and Combinatorial Aspects of Comparative Genomics*

Abstract. This document presents a survey of the statistical and combinatorial aspects of four areas of comparative genomics: gene order based measures of evolutionary distances between species, construction of phylogenetic trees, detection of horizontal transfer of genes, and detection of ancient whole genome duplications.     

On Optimality of Bayesian Wavelet Estimators

Abstract.  We investigate the asymptotic optimality of several Bayesian wavelet estimators, namely, posterior mean, posterior median and Bayes Factor, where the prior imposed on wavelet coefficients is a mixture of a mass function at zero and a Gaussian density. We show that in terms of the mean squared error, for the properly chosen hyperparameters of the prior, all the three resulting Bayesian wavelet estimators achieve optimal minimax rates within any prescribed Besov space     for p  ≥ 2. For 1 ≤  p  < 2, the Bayes Factor is still optimal for (2 s +2)/(2 s +1) ≤  p  < 2 and always outperforms the posterior mean and the posterior median that can achieve only the best possible rates for linear estimators in this case.     

Assessing goodness-of-fit of categorical regression models based on case-control data

Demonstrated equivalence between a categorical regression model based on case‐control data and an I‐sample semiparametric selection bias model leads to a new goodness‐of‐fit test. The proposed test statistic is an extension of an existing Kolmogorov–Smirnov‐type statistic and is the weighted average of the absolute differences between two estimated distribution functions in each response category. The paper establishes an optimal property for the maximum semiparametric likelihood estimator of the parameters in the I‐sample semiparametric selection bias model. It also presents a bootstrap procedure, some simulation results and an analysis of two real datasets.     

绿色供应链管理动力/压力影响模型实证研究

文章借鉴国外文献及国内环保专家的意见设计问卷进行调研,通过因子分析识别出中国制造企业绿色供应链管理的压力/动力和实践的主要因子.对因子进行相关分析和回归分析,根据数据结合国内外相关情况探讨动力/压力是如何影响绿色供应链管理实践,提出中国制造企业绿色供应链管理的动力/压力影响模型.     

统计执法的博弈分析

针对目前中国统计数据失真相当严重并引起社会各界普遍关注的现象，运用博弈论作为分析工具，引入重复博弈研究了统计执法中数据报方与查方的利益冲突关系，从统计执法的角度揭示了统计数据失真的主要原因，并提出了相应的五项对策。     

经贸院系英语教学模式探讨

分析目前我国英语教学与研究的误区。提出经贸院系学生应达到的英语水平目标；探讨经贸院系英语教学可采取六种教学模式。     

翻译:意识形态的建构、解构、重建与解读

翻译不仅是语言之间信息转换的文化活动,而且是一系列复杂的思维活动。意识形态以不同的语言形式隐藏在语篇之中,以各种隐蔽方式潜移默化地影响着作者写作、译者翻译以及读者解读文本。本文拟以批评语言学为主要方法论,强调翻译中语言分析和社会分析相结合的重要性,同时主张翻译研究应以语篇为单位,特别注意考察作者、译者、读者在翻译过程中的三元关系及其在意识形态的建构、解构、重建和解读过程中的作用,以便弄清翻译的本质并有效地解释那些"不忠"的现象。     

Does parents’ valuation of children’s health mimic their valuation of own health?

A single latent variable model of health status and therapeutic health care utilization is estimated for parents and own children of 6,557 US households. The equation system that identifies latent health status simultaneously determines a number of indicators of general health, including presence of morbidity symptoms, mobility limitations, medication needs, and utilization of therapeutic health care services. The main goal of the paper was to obtain an unbiased estimate of parents’ marginal substitution rate between own and child health. Results indicate that parents’ valuation of their children’s health exceeds their valuation of own health by almost twofold on average.