Composite quantile regression for massive datasets

Analysis of massive datasets is challenging owing to limitations of computer primary memory. Composite quantile regression (CQR) is a robust and efficient estimation method. In this paper, we extend CQR to massive datasets and propose a divide-and-conquer CQR method. The basic idea is to split the entire dataset into several blocks, applying the CQR method for data in each block, and finally combining these regression results via weighted average. The proposed approach significantly reduces the required amount of primary memory, and the resulting estimate will be as efficient as if the entire data set is analysed simultaneously. Moreover, to improve the efficiency of CQR, we propose a weighted CQR estimation approach. To achieve sparsity with high-dimensional covariates, we develop a variable selection procedure to select significant parametric components and prove the method possessing the oracle property. Both simulations and data analysis are conducted to illustrate the finite sample performance of the proposed methods.     

Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models

In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm, to sample model index variables in logistic mixed models involving a large number of explanatory variables. Furthermore, we proposed a simple proposal distribution for model index variables, and used a simulation study and real example to compare the performance of the HH algorithm with our proposed and existing proposal distributions. The results show that the HH algorithm with our proposed proposal distribution is a computationally efficient and reliable selection method.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

An algorithmic approach to construct D-optimal saturated designs for logistic model

In this paper, locally D-optimal saturated designs for a logistic model with one and two continuous input variables have been constructed by modifying the famous Fedorov exchange algorithm. A saturated design not only ensures the minimum number of runs in the design but also simplifies the row exchange computation. The basic idea is to exchange a design point with a point from the design space. The algorithm performs the best row exchange between design points and points form a candidate set representing the design space. Naturally, the resultant designs depend on the candidate set. For gain in precision, intuitively a candidate set with a larger number of points and the low discrepancy is desirable, but it increases the computational cost. Apart from the modification in row exchange computation, we propose implementing the algorithm in two stages. Initially, construct a design with a candidate set of affordable size and then later generate a new candidate set around the points of design searched in the former stage. In order to validate the optimality of constructed designs, we have used the general equivalence theorem. Algorithms for the construction of optimal designs have been implemented by developing suitable codes in R.     

基于HHM-RFRM的船舶航行风险评估方法研究

船舶航行风险是由各种风险因素交互作用产生。综合 HHM-RFRM 理论，构建船舶航行多维风险情景危险度测度模型，以风险因素耦合视角探讨船舶航行风险管理问题。在船舶航行多维风险情景危险度测度模型中结合贝叶斯定理，对船舶航行风险情景进行了定性与定量化过滤、评级。最后以大连港某从事商务活动的货船为例，验证了所提出方法的可行性。传统的风险评估方法只能评估单个风险因素对系统的影响，此评估方法克服了这一局限性，为船舶航行风险管理提供新的视角。     

逆向选择条件下带奖惩金的两期保险契约模型及其Pareto改进性研究

为了更有效的规避影响保险市场交易效率的逆向选择问题，本文分投保人风险类型为两种和多种情形建立了带奖惩金的两期保险契约模型，首次提出可以用奖励金和惩罚金有效甄别投保人的风险类型。该模型根据投保人第一个保险期内的索赔情况在第二个保险期对其进行奖励或惩罚，高风险类型的投保人如果选择为低风险类型投保人设计的保险契约，则其在第二阶段受到惩罚的概率要远远大于得到奖励的概率，即风险越高的投保人越害怕惩罚金，因此所建模型满足斯彭斯-莫里斯分离条件。带奖惩金的两期保险契约模型中保险公司的期望利润仍然为0，并不会给投保人带来额外的经济负担，却能够实现对传统部分保险契约简单重复两次的严格帕累托改进。最后采用一个算例说明了该模型的有效性。     

Production systems and supplier selection: a multi-phase process model

Abstract

Purpose: This paper presents a combined multi-phase supplier selection model. The process repeatedly revisits the criteria and sourcing decision as the development process continues. This enables a structured adoption of product and production system innovation from strategic suppliers, where previously the literature purely focuses on product innovation or cost reduction. Design/methodology/approach: The authors adopted an embedded researcher style, inductive, qualitative case study of an industrial supply cluster comprising a focal automotive company and its interaction with three different strategic stamping suppliers. Findings: Our contribution is the multi-phased production and product innovation process. This is an advance from traditional supplier selection and also an extension of ideas of supplier-located product development as it includes production system development, and complements the literature on working with strategic suppliers. Specifically, we explicitly articulate the previously unreported issue of whether a supplier chosen for its innovation capabilities at the start of the new product development process will also be the most appropriate supplier during the production system development phase, when an ability to work collaboratively may be the most important attribute, or in the large-scale production phase when an ability to manufacture at low unit cost may be most important. Originality/value: The paper identifies a multi-phase approach to tendering within a fixed body of strategic suppliers which seeks to identify the optimum technological and process decisions as well as the traditional supplier sourcing choice. These areas have not been combined before and generate a valuable approach for firms to adopt as well as for researchers to extend our understanding of a highly complex process.     

吉伯德-萨特思韦特防策略不可能性定理的逻辑初探

自20世纪70年代吉伯德-萨特思韦特防策略不可能性定理确立后，引起了数学、经济学、计算机科学和哲学等诸多领域中学者的广泛关注。然而，在国内外文献中，对吉伯德-萨特思韦特防策略不可能性定理的逻辑研究还比较少。基于此，借助公理化的方法探讨吉伯德-萨特思韦特防策略不可能性定理，并形式化地给出定理的内容和证明，力图把精细的逻辑推理应用于复杂的过程分析中，使防策略投票这一过程形式化、清晰化，以期能指导人们进行合理、有效的决策活动。     

The dual-continuum approach: An extension of the contingency theory of strategic conflict management

This article examines the limitations of using a single advocacy-accommodation continuum when depicting organizational stance and movement in conflict situations (Cancel et al., 1997). The authors argue that advocacy and accommodation can vary independently in response to a variety of contingent factors. To comprehensively capture the locations and motions that are possible in organization-public conflicts, a two-continuum approach is proposed. Separate assessment of the effects of contingency variables on advocacy and accommodation can be used to accurately locate organizational stance along advocacy and accommodation continua. Strategic alternatives that cannot be depicted using a single continuum (such as favorable alignment of organization advocacy/public accommodation or unilateral change on the part of one party) can be captured if separate continua are employed. By depicting initial stances and desired directions of movement for both the organization and public, the dual-continuum approach can also provide practical guidance to public relations professionals in selecting strategies for achieving preferred outcomes and evaluating their effectiveness.