���������ݻع��������

With the advance of computer storage capacity and online observation technique, more and more data are collected with curves and images. The most two important feature of curve and image data are high-dimension and high correlation between adjacent data. Functional data analysis has more advantage in deal with these data, which can not be treated by traditional multivariate statistics methods. Recently, a variety of functional data methods have been developed, including curve alignment, principal component analysis, regression, classification and clustering. In this paper, we mainly introduce the origins,development and recent process of functional data. Specifically, we firstly introduce the notion of functional data. Secondly, functional principal component analysis has been presented. Then, this paper is devoted to introduce estimation, variable selection and hypothesis testing of functional regression models. Lastly, the paper concludes with a brief discussion of future directions.     

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。     

基于一致性的制造业企业伙伴选择多属性决策模型研究——合作创新视角

为解决制造业企业合作创新伙伴选择问题,将单一评价方法与组合评价方法相结合,提出了基于一致性的制造业企业合作创新伙伴选择组合评价方法。在构建制造业企业合作创新伙伴选择评价指标体系基础上,应用基于一致性的组合评价方法对某汽车制造业企业合作创新伙伴选择组合评价,得到了一致性和收敛性的评价结论,验证了基于一致性方法的科学性、合理性和有效性。基于一致性的组合评价方法一定程度上解决了组合评价结论的非一致性问题,不仅可以达到组合评价结果取长补短的效果,而且能够充分利用多层次信息,更加贴近评价对象的全貌,为多属性决策问题提供了新思路。     

Difference operators,Green's matrix,sampling theory and applications in signal processing

This paper introduces sampling representations for discrete signals arising from self adjoint difference operators with mixed boundary conditions. The theory of linear operators on finite-dimensional inner product spaces is employed to study the second-order difference operators. We give necessary and sufficient conditions that make the operators self adjoint. The equivalence between the difference operator and a Hermitian Green's matrix is established. Sampling theorems are derived for discrete transforms associated with the difference operator. The results are exhibited via illustrative examples, involving sampling representations for the discrete Hartley transform. Families of discrete fractional Fourier-type transforms are introduced with an application to image encryption.     

An adaptive O(log n)‐optimal policy for the online selection of a monotone subsequence from a random sample

Given a sequence of n independent random variables with common continuous distribution, we propose a simple adaptive online policy that selects a monotone increasing subsequence. We show that the expected number of monotone increasing selections made by such a policy is within of optimal. Our construction provides a direct and natural way for proving the ‐optimality gap. An earlier proof of the same result made crucial use of a key inequality of Bruss and Delbaen [5] and of de‐Poissonization.     

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.     

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.