Evaluating functional covariate‐environment interactions in the Cox regression model

Children exposed to mixtures of endocrine disrupting compounds such as phthalates are at high risk of experiencing significant friction in their growth and sexual maturation. This article is primarily motivated by a study that aims to assess the toxicants‐modified effects of risk factors related to the hazards of early or delayed onset of puberty among children living in Mexico City. To address the hypothesis of potential nonlinear modification of covariate effects, we propose a new Cox regression model with multiple functional covariate‐environment interactions, which allows covariate effects to be altered nonlinearly by mixtures of exposed toxicants. This new class of models is rather flexible and includes many existing semiparametric Cox models as special cases. To achieve efficient estimation, we develop the global partial likelihood method of inference, in which we establish key large‐sample results, including estimation consistency, asymptotic normality, semiparametric efficiency and the generalized likelihood ratio test for both parameters and nonparametric functions. The proposed methodology is examined via simulation studies and applied to the analysis of the motivating data, where maternal exposures to phthalates during the third trimester of pregnancy are found to be important risk modifiers for the age of attaining the first stage of puberty. The Canadian Journal of Statistics 47: 204–221; 2019 © 2019 Statistical Society of Canada     

Enterprise systems and performance of future city logistics

Abstract

Future cities are driven by the developments in Information and Communication Technology to support the triple bottom line requirements of sustainability. Logistics will play a critical role in future cities due to the increasingly micro-dynamic nature of socio-economics of these cities and globalised production and consumption patterns. Enterprise systems (ES), the founding applications to automate and integrate different business processes, will be the key enablers for providing the necessary support required by the future city logistics. However, the implementation of ES and realisation of proposed benefits within the estimated time frame is challenging due to the huge resource requirements in terms of manpower, budget and time. This study takes the antecedent (critical success factors) approach for the implementation process to establish a success predictive model for the realisation of ES benefits. The partial least square regression has been used to test the model. Moreover, this study explores the impact of ES benefits on the logistics performance indicators to explore the synergies between them. This is fundamentally important because future city logistics will rely heavily on appropriately designed and implemented (enterprise) information systems.     

Link sampling for attributes

A few lot-by-lot acceptance sampling procedures for attributes are proposed as alternatives to the usual double sampling. In these schemes whenever a second sample is needed, the sample information from neighbouring lots is used. The new plans have the DC identical to that of the comparable double sampling plan. The primary advantage of these plans is a reduction in cost due to a smaller ASN. An empirical study which investigates the effect of sudden shifts in quality level on the probability of acceptance and ARL under the proposed plans is included     

部分信息下实物期权的定价和风险对冲

当前所有实物期权理论研究都是基于完全信息(full information)假设.本文则通过研究投资者在部分信息(partial information)下极大化无限期消费效用的最优投资消费问题,得出实物期权的消费效用无差别价格.通过控制系统的分离原理,运用Kalman滤波技术和随机控制方法,得到了CARA效用函数情形下实物期权的自由边界偏微分方程.利用有限差分法,解得实物期权的隐含价值及最优执行水平从而得到最优投资消费策略和效用函数的数值解.通过蒙特卡洛模拟,给出了投资者在完全信息和部分信息下的动态决策差异,并且通过比较两种信息水平下的投资者福利给出了信息价值的测算.     

The Dantzig Selector in Cox's Proportional Hazards Model

Abstract. The Dantzig selector (DS) is a recent approach of estimation in high‐dimensional linear regression models with a large number of explanatory variables and a relatively small number of observations. As in the least absolute shrinkage and selection operator (LASSO), this approach sets certain regression coefficients exactly to zero, thus performing variable selection. However, such a framework, contrary to the LASSO, has never been used in regression models for survival data with censoring. A key motivation of this article is to study the estimation problem for Cox's proportional hazards (PH) function regression models using a framework that extends the theory, the computational advantages and the optimal asymptotic rate properties of the DS to the class of Cox's PH under appropriate sparsity scenarios. We perform a detailed simulation study to compare our approach with other methods and illustrate it on a well‐known microarray gene expression data set for predicting survival from gene expressions.     

面向评价局部环境的群评价长效机制分析

在群组评价问题中,称评价参与者(即节点集)及参与者之间的评价关系构成群组评价局部环境,即评价群体的网络结构。将评价局部环境看作是一个有限状态、不可约且非周期性马尔可夫过程,使得群组评价问题水平维度的评价局部环境基于时间维度进行拓展,证明参与者对评价关系的调整可以改善群体网络结构的稳定性;具有评价关系的参与者状态正相关;参与者不满意效用状态持续时间的增加降低了其达到满意效用状态的概率。在此基础上,讨论以被评价对象为主要研究对象设置群组评价方法,根据三种常见的评价局部环境状态,考察评价群体的群体性行为或认知趋势性特征,在此基础上讨论评价目标的可执行性。允许有关被评价对象的新信息注入群组评价系统,在长期分析评价群体的群体性评价行为的趋势特征,据此构建面向评价局部环境的群组评价方法,分析评价目标的可执行性,以期使群组评价行为具有稳定性和可预见性。     

论票据一部付款

承认票据一部付款既有利于持票人利益的维护,对于票据债务人也深具实益。在不能进行票据全部付款时,承认一部付款也是维护票据信用的有效手段。我国《票据法》不承认一部付款,这不仅与民事上部分履行的理念不相符合,也与国际立法例背道而驰,有待修正。     

Oracally efficient spline-backfitted kernel smoothing of additive partial linear measurement error model

Abstract

We consider statistical inference for additive partial linear models when the linear covariate is measured with error. A bias-corrected spline-backfitted kernel smoothing method is proposed. Under mild assumptions, the proposed component function and parameter estimator are oracally efficient and fast to compute. The nonparametric function estimator’s pointwise distribution is asymptotically equivalent to an function estimator in partial linear model. Finite-sample performance of the proposed estimators is assessed by simulation experiments. The proposed methods are applied to Boston house data set.     

数据包络分析方法中决策单元偏序关系的建立

针对基于偏序集理论的数据包络分析方法中无法给出非规模收益不变模型中决策单元偏序关系的缺陷,提供了三种常见数据包络分析模型中偏序关系的建立理论及偏序关系确定算法。该算法能够给出各个决策单元之间的偏序关系矩阵的同时也能够给出偏序关系图,这为决策者提供了更多的决策依据。最终将这一方法应用到了某省自然科学基金评价问题中。