A new algorithm for fixed design regression and denoising

In this paper, we present a new algorithm to estimate a regression function in a fixed design regression model, by piecewise (standard and trigonometric) polynomials computed with an automatic choice of the knots of the subdivision and of the degrees of the polynomials on each sub-interval. First we give the theoretical background underlying the method: the theoretical performances of our penalized least-squares estimator are based on non-asymptotic evaluations of a mean-square type risk. Then we explain how the algorithm is built and possibly accelerated (to face the case when the number of observations is great), how the penalty term is chosen and why it contains some constants requiring an empirical calibration. Lastly, a comparison with some well-known or recent wavelet methods is made: this brings out that our algorithm behaves in a very competitive way in term of denoising and of compression.     

Selecting distances in arrangements of hyperplanes spanned by points

In this paper we consider a problem of distance selection in the arrangement of hyperplanes induced by n given points. Given a set of n points in d-dimensional space and a number k, , determine the hyperplane that is spanned by d points and at distance ranked by k from the origin. For the planar case we present an O(nlog²n) runtime algorithm using parametric search partly different from the usual approach [N. Megiddo, J. ACM 30 (1983) 852]. We establish a connection between this problem in 3-d and the well-known 3SUM problem using an auxiliary problem of counting the number of vertices in the arrangement of n planes that lie between two sheets of a hyperboloid. We show that the 3-d problem is almost 3SUM-hard and solve it by an O(n²log²n) runtime algorithm. We generalize these results to the d-dimensional (d4) space and consider also a problem of enumerating distances.     

拓扑空间中的连续逼近选择和几乎不动点

本文研究连续逼近选择问题 研究结果表明 ,几乎下半连续的集值映射有连续逼近选择 .从而 ,著名的Michael连续逼近选择定理被改进 .应用这个结果 ,一个几乎不动点定理被获得     

共同治理结构下企业所有权配置的进化博弈分析

本文运用进化博弈论的方法,对有限理性的利益相关者在共同治理结构下参与企业所有权配置并达到纳什均衡的进化博弈过程进行分析,并通过算例说明了这是一个各方相互沟通和反复博弈的动态过程,逐步实现一致性的企业所有权配置的纳什均衡.     

Practical arbitrage‐free scenario tree reduction methods and their applications in financial optimization

We construct an arbitrage‐free scenario tree reduction model, from which some arbitrage‐free scenario tree reduction algorithms are designed. They ensure that the reduced scenario trees are arbitrage free. Numerical results show the practicality and efficiency of the proposed algorithms. Results for multistage portfolio selection problems demonstrate the necessity and importance for guaranteeing that the reduced scenario trees are arbitrage free, as well as the practicality of the proposed arbitrage‐free scenario tree reduction algorithms for financial optimization.     

Non-Convex Global Minimization and False Discovery Rate Control for the TREX

The TREX is a recently introduced method for performing sparse high-dimensional regression. Despite its statistical promise as an alternative to the lasso, square-root lasso, and scaled lasso, the TREX is computationally challenging in that it requires solving a nonconvex optimization problem. This article shows a remarkable result: despite the nonconvexity of the TREX problem, there exists a polynomial-time algorithm that is guaranteed to find the global minimum. This result adds the TREX to a very short list of nonconvex optimization problems that can be globally optimized (principal components analysis being a famous example). After deriving and developing this new approach, we demonstrate that (i) the ability of the preexisting TREX heuristic to reach the global minimum is strongly dependent on the difficulty of the underlying statistical problem, (ii) the new polynomial-time algorithm for TREX permits a novel variable ranking and selection scheme, (iii) this scheme can be incorporated into a rule that controls the false discovery rate (FDR) of included features in the model. To achieve this last aim, we provide an extension of the results of Barber and Candes to establish that the knockoff filter framework can be applied to the TREX. This investigation thus provides both a rare case study of a heuristic for nonconvex optimization and a novel way of exploiting nonconvexity for statistical inference.     

Optimal Decisions in a Time Priority Queue

We show how the position of a limit order (LO) in the queue influences the decision of whether to cancel the order or let it rest. Using ultra-high-frequency data from the Nasdaq exchange, we perform empirical analysis on various LO book events and propose novel ways for modelling some of these events, including cancellation of LOs in various positions and size of market orders. Based on our empirical findings, we develop a queuing model that captures stylized facts on the data. This model includes a distinct feature which allows for a potentially random effect due to the agent’s impulse control. We apply the queuing model in an algorithmic trading setting by considering an agent maximizing her expected utility through placing and cancelling of LOs. The agent’s optimal strategy is presented after calibrating the model to real data. A simulation study shows that for the same level of standard deviation of terminal wealth, the optimal strategy has a 2.5% higher mean compared to a strategy which ignores the effect of position, or an 8.8% lower standard deviation for the same level of mean. This extra gain stems from posting an LO during adverse conditions and obtaining a good queue position before conditions become favourable.     

考虑供应链风险和绩效的供应商选择的模糊动态非线性多目标规划模型

基于供应链风险和供应链绩效的模糊性和供应商选择问题的动态性,本文考虑供应链风险和供应链绩效作为模糊变量,讨论如何给生产商一个满意的动态多目标供应商选择方案,确定供应链风险和总成本最小,以及供应链绩效最大。然后对该问题提出了一个动态多目标多产品供应商选择模型,该模型是首次同时考虑供应商选择,订单分配,供应链风险和供应链绩效的一个模糊动态非线性多目标规划模型。为了去模糊化和求解该模型,给出了一个风险和绩效的模糊评估法。最后给出一个数值算例验证了该模型的可行性,为决策者选择供应商提供了理论依据。     

网络舆情的研究与发展—以涉警网络舆情为例

网络舆情传播速度快、波及范围广,同时具有很强的突发性、不确定性、难以控制等特点,对相关部门进行涉警网络舆情干预和引导带来巨大挑战。目前有关网络舆情引导、管控机制、应对策略研究等成果丰富、但是对网络舆情危机的演变和干预多停留在定性分析之上、定量分析研究相对欠缺。现有网络舆情研究多针对舆情发展某一阶段进行研究,未结合网络舆情发展的多阶段属性展开全生命周期研究,为数不多的网络舆情多阶段干预决策机制研究也比较粗略,缺乏一套科学、规范的干预决策机制作为支撑。因此,结合网络舆情的多阶段属性、应用定量分析方法,是未来网络舆情干预决策机制研究的方向之一。本文通过对网络舆情发展过程的描述,给出一定量的状态表述和行动刻画,构建一个分类多阶段决策模型框架。     

顾客参与研发过程的价值共创机理与策略研究

顾客参与企业研发是产品价值创造的最初始过程,不仅提高产品接受度、满足顾客需求,而且为企业增加收入,但同时也产生相应成本。文章探究顾客参与研发的系统中,顾客、企业和产品的价值共创行为以及企业决策如何影响三者价值;立足多目标视角,充分考虑企业与顾客的收益、成本,采用系统动力学方法建立顾客参与研发过程的动态价值共创模型,全面探究系统内部共创机理。在模型基础上,重点就企业产品价格、交互次数以及激励资金比例等策略进行仿真,使企业、顾客及产品实现价值共创。结论如下:(1)随着顾客、企业交互次数的增多,顾客参与研发过程同样具备一般产品生命周期导入期、成长期、成熟期以及衰退期的特点。(2)系统存在最优交互次数;且最优交互次数随着研发投入比例减少而增加。(3)在不考虑参与顾客获取激励资金的情况下,企业针对不同顾客群(参与研发顾客与普通购买顾客)进行定价,在最优交互次数状态下,统一定价模式优于差别定价模式,将为企业带来更大的收益。