《红楼梦》亲属语译出中文化内涵的缺失

《红楼梦》中人物繁多,称谓复杂,在众多的中国古典文学名著中最能反映中国传统文化对称谓语的影响。本文以杨宪益夫妇所译之《红楼梦》为例,通过其亲属称谓词的翻译比较,来看其译出过程中造成的文化内涵的缺失,并试着探讨其在一定范围内的弥合策略。     

知识型员工流失的深层研究——基于心理契约的视角

知识型员工由于其地位和作用的特殊性,一直是企业竞争的主要对象,其流动率也一直较高。知识型员工流失在很大程度上是心理契约违背的必然结果,因此,重构和优化心理契约至关重要。具体而言,在招聘、入职、变动和离职四个阶段,企业应分别采取真实岗位预视、建立动态档案、加强沟通、及时补偿和离职面谈等措施,防止知识型员工流失。     

媒体参与下群体性突发事件的演化博弈

借助演化经济学研究工具,考虑媒体参与对群体行为的影响,构建包括媒体敏感因子、声誉损失和冲突处置成本等参数在内的群体性突发事件演化博弈模型,并对群体策略选择进行演化动态稳定性分析,通过数值仿真方法分析决策参数的不同取值和初始条件的改变对演化结果的影响及其演化路径。结果表明：媒体敏感因子和声誉损失是影响群体性突发事件动态演化的关键因素,媒体敏感因子和声誉损失较高时,无论是强势群体还是弱势群体,都会选择合作策略,协商解决利益分配问题。政府应充分利用和发挥媒体监督职能对群体策略选择的引导作用,促使群体性突发事件跳出“不良锁定”状态,朝着理想状态演化。     

基于经济性设计的过程能力指数优化

本文阐述了过程能力指数的发展形式及存在的问题，探索了过程能力指数的优化途径，建立了基于质量损失与过程控制成本之和最小化的过程能力指数模型．     

集装箱运输货损责任法律问题研究

从对集装箱性质的分析入手 ,通过对与集装箱运输货物责任相关问题的研究及各国实践做法的介绍 ,试图使集装箱运输货损责任的认定清晰化、简单化。同时对现有法律中存在的问题加以说明 ,以使法律能够尽快被完善 ,尽快适应集装箱运输方式快速发展的步伐     

The empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior under Stein's loss function

For the hierarchical Poisson and gamma model, we calculate the Bayes posterior estimator of the parameter of the Poisson distribution under Stein's loss function which penalizes gross overestimation and gross underestimation equally and the corresponding Posterior Expected Stein's Loss (PESL). We also obtain the Bayes posterior estimator of the parameter under the squared error loss and the corresponding PESL. Moreover, we obtain the empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior by two methods. In numerical simulations, we have illustrated: The two inequalities of the Bayes posterior estimators and the PESLs; the moment estimators and the Maximum Likelihood Estimators (MLEs) are consistent estimators of the hyperparameters; the goodness-of-fit of the model to the simulated data. The numerical results indicate that the MLEs are better than the moment estimators when estimating the hyperparameters. Finally, we exploit the attendance data on 314 high school juniors from two urban high schools to illustrate our theoretical studies.     

On evaluating how well a biomarker can predict treatment response with survival data

One of the objectives of personalized medicine is to take treatment decisions based on a biomarker measurement. Therefore, it is often interesting to evaluate how well a biomarker can predict the response to a treatment. To do so, a popular methodology consists of using a regression model and testing for an interaction between treatment assignment and biomarker. However, the existence of an interaction is not sufficient for a biomarker to be predictive. It is only necessary. Hence, the use of the marker‐by‐treatment predictiveness curve has been recommended. In addition to evaluate how well a single continuous biomarker predicts treatment response, it can further help to define an optimal threshold. This curve displays the risk of a binary outcome as a function of the quantiles of the biomarker, for each treatment group. Methods that assume a binary outcome or rely on a proportional hazard model for a time‐to‐event outcome have been proposed to estimate this curve. In this work, we propose some extensions for censored data. They rely on a time‐dependent logistic model, and we propose to estimate this model via inverse probability of censoring weighting. We present simulations results and three applications to prostate cancer, liver cirrhosis, and lung cancer data. They suggest that a large number of events need to be observed to define a threshold with sufficient accuracy for clinical usefulness. They also illustrate that when the treatment effect varies with the time horizon which defines the outcome, then the optimal threshold also depends on this time horizon.     

Oracle Inequalities for Convex Loss Functions with Nonlinear Targets

This article considers penalized empirical loss minimization of convex loss functions with unknown target functions. Using the elastic net penalty, of which the Least Absolute Shrinkage and Selection Operator (Lasso) is a special case, we establish a finite sample oracle inequality which bounds the loss of our estimator from above with high probability. If the unknown target is linear, this inequality also provides an upper bound of the estimation error of the estimated parameter vector. Next, we use the non-asymptotic results to show that the excess loss of our estimator is asymptotically of the same order as that of the oracle. If the target is linear, we give sufficient conditions for consistency of the estimated parameter vector. We briefly discuss how a thresholded version of our estimator can be used to perform consistent variable selection. We give two examples of loss functions covered by our framework.     

Temporal Disaggregation: Methods,Information Loss,and Diagnostics

This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to exploit related independent variables in the first stage and a state–space model to disaggregate the residual from the regression in the second stage. For the purpose of providing a set of practical criteria for assessing the disaggregation performance, we measure the information loss that occurs during temporal aggregation while examining what effects take place when aggregating data. To validate the proposed framework, we implement Monte Carlo simulations and provide two empirical studies. Supplementary materials for this article are available online.