Inferences of variance function - a parametric robust way

Tsou (2003a) proposed a parametric procedure for making robust inference for mean regression parameters in the context of generalized linear models. This robust procedure is extended to model variance heterogeneity. The normal working model is adjusted to become asymptotically robust for inference about regression parameters of the variance function for practically all continuous response variables. The connection between the novel robust variance regression model and the estimating equations approach is also provided.     

An oracle-based algorithm for robust planning of production routing problems in closed-loop supply chains of beverage glass bottles

The demand for glass bottles is exhibiting an upward trend over time. The manufacturing of glass bottles is costlier in terms of time and resources and is associated with a higher level of heat generation and environmental pollution compared to recycling processes. In response to the aforementioned challenges, companies that use glass bottles need to implement strategies to manage their reverse supply chains in conjunction with their traditional supply chains, as the economic and environmental benefits of returned products are unquestionable. Closed-loop supply chains (CLSCs) integrate forward and reverse flows of products and information. This integration helps companies to have a broader view of the whole chain. Despite these advantages, managing CLSCs can be challenging as they are exposed to many uncertainties regarding supply and demand processes, travel times, and quantity/quality of returned products.In this study, we consider the production planning, inventory management, and vehicle routing decisions of a CLSC of beverage glass bottles. We propose an MILP model and rely on a multi-stage adjustable robust optimization (ARO) formulation to deal with the randomness in both the demand for filled bottles and the requests for pickups of empty bottles. We develop an exact oracle-based algorithm to solve the ARO problem and propose a heuristic search algorithm to reduce the solution time. Our numerical experiments not only show the incompetency of the customary method, namely the affine decision rule approach, but also illustrate how our algorithms can solve the small-size problems and significantly improve the quality of the obtained solution for large problems. Furthermore, our numerical results show that robust plans tend to be sparse, meaning the routes are chosen so that empty bottles are transported to production sites in such a way that fewer new bottles need to be ordered. Thus, robust planning makes the CLSCs more environmentally friendly.     

基于稳健稀疏主成分的经济增长影响因素分析

从传统生产要素、制度、金融和经济结构等四个方面选取了19个影响中国经济增长的变量,运用稳健稀疏主成分方法进行实证分析。结果表明物质资本、城镇化率、金融深度、城镇居民消费结构、基尼系数、技术水平已成为促进中国经济增长的主要动力,但东、中、西部经济增长的主要影响因素互不相同。在此基础上就如何进一步促进中国经济增长和区域经济均衡发展提出了若干政策建议。     

军队技术人才培养规划的鲁棒优化研究

本文针对军队发展带来技术人才需求的动态变化,通过将技术人才各方面素质需求的不确定性离散成三种不同情景,采用鲁棒优化方法构建了军队人才培养的鲁棒优化模型,以军队信息化建设人才需求为背景,以某部人才队伍为研究对象进行了实证求解,并比较了基于单一情景的规划结果与鲁棒优化模型的结果。研究结果表明,鲁棒优化模型对于军队人才培养规划制定具有更好的稳定性和可行性。     

基于鲁棒优化模型的项目调度策略遗传算法

针对任务工期不确定的资源受限项目调度问题,提出了求解该问题的鲁棒优化数学模型。在随机规划基础上,针对该鲁棒优化模型设计了遗传算法。算法通过多种任务优先规则生成初始种群以保证种群多样性。应用该遗传算法对项目实例进行了求解,分析表明所求得的项目调度策略能够有效应对任务工期不确定性导致的随机差异,具有较强的鲁棒性。参数分析表明决策者可以通过调节模型权重系数有效平衡解的可行性与最优性,有助于决策者根据风险偏好进行选择。     

不确定需求下二级供应链中的鲁棒定价问题

对由一个制造商和一个零售商组成的单一产品二级供应链,在市场潜在需求和价格敏感系数不确定的情况下,分析制造商和零售商的鲁棒定价决策。首先考虑了零售商掌握完全信息而制造商仅已知各参数区间的不对称信息情形,建立Stackelberg鲁棒博弈模型并对模型的均衡解进行探讨,进一步通过对解的分析和对比讨论了不对称信息下零售商的决策优势。然后研究了制造商和零售商都仅已知各参数区间的不完全信息情形,给出Stackelberg鲁棒博弈模型,求得其均衡解并与完全信息下的情形以及不对称信息下的情形进行比较,分别得到制造商和零售商在不完全信息下所获得的利润比在完全信息下所获得利润高的条件,并证明了制造商在不完全信息下所获得的利润比在不对称信息下获得的利润高,而零售商则刚好相反。最后给出算例分析,对所得到的解和结论做一些补充。     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Influence diagnostics in the capital asset pricing model under elliptical distributions

In this paper we consider the Capital Asset Pricing Model under Elliptical (symmetric) Distributions. This class of distributions, which contains the normal distribution, t, contaminated normal and power exponential, among others, offers a more flexible framework for modelling asset prices or returns. In order to analyze the sensibility to possible outliers and/or atypical returns of the maximum likelihood estimators, the local influence method was implemented. The results are illustrated by using a set of shares from companies who trade in the Chilean Stock Market. Our main conclusion is that symmetric distributions having heavier tails than those of the normal distribution, especially the t distribution with small degrees of freedom, show a better fit and allow the reduction of the influence of atypical returns in the maximum likelihood estimators.     

Robust methods for generalized linear models with nonignorable missing covariates

The EM algorithm is often used for finding the maximum likelihood estimates in generalized linear models with incomplete data. In this article, the author presents a robust method in the framework of the maximum likelihood estimation for fitting generalized linear models when nonignorable covariates are missing. His robust approach is useful for downweighting any influential observations when estimating the model parameters. To avoid computational problems involving irreducibly high‐dimensional integrals, he adopts a Metropolis‐Hastings algorithm based on a Markov chain sampling method. He carries out simulations to investigate the behaviour of the robust estimates in the presence of outliers and missing covariates; furthermore, he compares these estimates to the classical maximum likelihood estimates. Finally, he illustrates his approach using data on the occurrence of delirium in patients operated on for abdominal aortic aneurysm.     

Automatic clustering algorithm for fuzzy data

Coppi et al. [7 R. Coppi, P. D'Urso, and P. Giordani, Fuzzy and possibilistic clustering for fuzzy data, Comput. Stat. Data Anal. 56 (2012), pp. 915–927. doi: 10.1016/j.csda.2010.09.013[Crossref], [Web of Science ®] , [Google Scholar]] applied Yang and Wu's [20 M.-S. Yang and K.-L. Wu, Unsupervised possibilistic clustering, Pattern Recognit. 30 (2006), pp. 5–21. doi: 10.1016/j.patcog.2005.07.005[Crossref], [Web of Science ®] , [Google Scholar]] idea to propose a possibilistic k-means (PkM) clustering algorithm for LR-type fuzzy numbers. The memberships in the objective function of PkM no longer need to satisfy the constraint in fuzzy k-means that of a data point across classes sum to one. However, the clustering performance of PkM depends on the initializations and weighting exponent. In this paper, we propose a robust clustering method based on a self-updating procedure. The proposed algorithm not only solves the initialization problems but also obtains a good clustering result. Several numerical examples also demonstrate the effectiveness and accuracy of the proposed clustering method, especially the robustness to initial values and noise. Finally, three real fuzzy data sets are used to illustrate the superiority of this proposed algorithm.