Principal component histograms from interval-valued observations

The focus of this paper is to propose an approach to construct histogram values for the principal components of interval-valued observations. Le-Rademacher and Billard (J Comput Graph Stat 21:413–432, 2012) show that for a principal component analysis on interval-valued observations, the resulting observations in principal component space are polytopes formed by the convex hulls of linearly transformed vertices of the observed hyper-rectangles. In this paper, we propose an algorithm to translate these polytopes into histogram-valued data to provide numerical values for the principal components to be used as input in further analysis. Other existing methods of principal component analysis for interval-valued data construct the principal components, themselves, as intervals which implicitly assume that all values within an observation are uniformly distributed along the principal components axes. However, this assumption is only true in special cases where the variables in the dataset are mutually uncorrelated. Representation of the principal components as histogram values proposed herein more accurately reflects the variation in the internal structure of the observations in a principal component space. As a consequence, subsequent analyses using histogram-valued principal components as input result in improved accuracy.     

Exponential separation and principal Lyapunov exponent/spectrum for random/nonautonomous parabolic equations

The purpose of the paper is to extend the principal eigenvalue and principal eigenfunction theory for time independent and periodic parabolic equations to random and general nonautonomous ones. In the random case, a notion of principal Lyapunov exponent serving as an analog of principal eigenvalue is introduced. It is shown that the principal Lyapunov exponent is deterministic and of simple multiplicity. It is also shown that there is a one-dimensional invariant random subbundle corresponding to the solutions that are globally defined and of the same sign, which serves as an analog of principal eigenfunction. In addition, monotonicity of the principal Lyapunov exponent with respect to the zero-order terms both in the equation and in the boundary condition is proved. When the second- and first-order terms are deterministic, it is proved that the principal Lyapunov exponent is greater than or equal to the principal eigenvalue of the associated time-averaged equation. In the general nonautonomous case, the concepts of principal spectrum, which serves as an analog of principal eigenvalue, and principal Lyapunov exponents are introduced. As is known, the principal spectrum is a compact interval. It is proved in the paper that the principal spectrum contains all the principal Lyapunov exponents. When the second and first-order terms are time independent, a lower estimate of the infimum of the principal spectrum is given in terms of an associated time-averaged equation.     

Principal differential analysis of the Aneurisk65 data set

We explore the use of principal differential analysis as a tool for performing dimensional reduction of functional data sets. In particular, we compare the results provided by principal differential analysis and by functional principal component analysis in the dimensional reduction of three synthetic data sets, and of a real data set concerning 65 three-dimensional cerebral geometries, the AneuRisk65 data set. The analyses show that principal differential analysis can provide an alternative and effective representation of functional data, easily interpretable in terms of exponential, sinusoidal, or damped-sinusoidal functions and providing a different insight to the functional data set under investigation. Moreover, in the analysis of the AneuRisk65 data set, principal differential analysis is able to detect interesting features of the data, such as the rippling effect of the vessel surface, that functional principal component analysis is not able to detect.     

我国上市公司资本结构影响因素实证分析

本文选取了可能影响企业资本结构的多个指标变量进行分析 ,利用主成分分析提供的方法将变量综合成彼此互不相关的少数几个主成分。再用主成分 (作为回归自变量 )对企业的 5种资本负债比 (作为回归因变量 )进行多元回归分析 ,得出了影响企业资本结构的主要因素 ,以及这些因素与企业资本结构之间的关系 ,为企业确定资本结构提供参考依据 ,为企业财务决策提供支持。     

Truncations of principal geometries

We investigate the class of principal pregeometries (free simplicial geometries with spanning simplex) which form an important subclass of the class of transversal pregeometries (free simplicial geometries). We give a coordinate-free method for imbedding a transversal pregeometry on a simplex as a free simplicial pregeometry which makes use only of the set-theoretic properties of a presentation of the transversal pregeometry. We introduce the notion of an (r, k)-principal set as a generalization of principal basis and prove the collection of (r, k)-principal sets of a rank k pregeometry, if non-empty, are the bases of another pregeometry whose structure is determined. An algorithm for constructing principal sets is given. We then characterize truncations of principal geometries in terms of the existence of a principal set. We do this by erecting a given pregeometry to a free simplicial pregeometry with spanning simplex. The erection is the freest of all erections of the given pregeometry.     

主成分分析在中国上市公司综合评价中的作用

主要介绍了主成分分析在上市公司综合评价中的作用 .主成分分析作为一种客观赋权的方法 .权数是能随着宏观经济环境变化而变化的动态权数体系 ,因为主成分分析所应用的数据来源于上市公司年度报告所提供的财务指标 .它主要对所选择的 40只绩优股 ,进行横向的比较 ,并根据第一主成分得分进行排序 ,给广大的投资者提供参考 .     

Congruence computations in principal arithmetical varieties

This paper is a continuation of the earlier paper by the same authors in which a primary result was that every arithmetical affine complete variety of finite type is a principal arithmetical variety with respect to an appropriately chosen Pixley term. The paper begins by presenting an extension of this result to all finitely generated congruences and, as an example, constructs a closed form solution formula for any finitely presented system of pairwise compatible congruences (the Chinese remainder theorem). It is also shown that in all such varieties the meet of principal congruences is also principal, and finally, if a minimal generating algebra of the variety is regular, it is shown that the variety is also regular and the join of principal congruences is again principal.     

Principal Dynamical Components

A procedure is proposed for a dimension reduction in time series. Similarly to principal components, the procedure seeks a low‐dimensional manifold that minimizes information loss. Unlike principal components, however, the procedure involves dynamical considerations through the proposal of a predictive dynamical model in the reduced manifold. Hence the minimization of the uncertainty is not only over the choice of a reduced manifold, as in principal components, but also over the parameters of the dynamical model, as in autoregressive analysis and principal interaction patterns. Further generalizations are provided to nonautonomous and non‐Markovian scenarios, which are then applied to historical sea‐surface temperature data. © 2012 Wiley Periodicals, Inc.     

Principal structure of submodular systems and hitchcock-type independent flows

This paper discusses the principal structure of submodular systems due to S. Fujishige. It is shown that the principal structure is the coarsest decomposition that is finer than any decomposition induced by the principal partition with respect to a minimal nonnegative superbase. The concept of Hitchcock-type independent flow is introduced so that previously known results on the principal structures for bipartite matchings, layered mixed matrices and independent matchings can be understood as applications of the present result.     

Cluster Algebras of Finite Type via Coxeter Elements and Principal Minors

We give a uniform geometric realization for the cluster algebra of an arbitrary finite type with principal coefficients at an arbitrary acyclic seed. This algebra is realized as the coordinate ring of a certain reduced double Bruhat cell in the simply connected semisimple algebraic group of the same Cartan–Killing type. In this realization, the cluster variables appear as certain (generalized) principal minors.     

Semistable and numerically effective principal (Higgs) bundles

We study Miyaoka-type semistability criteria for principal Higgs G-bundles E on complex projective manifolds of any dimension. We prove that E has the property of being semistable after pullback to any projective curve if and only if certain line bundles, obtained from some characters of the parabolic subgroups of G, are numerically effective. One also proves that these conditions are met for semistable principal Higgs bundles whose adjoint bundle has vanishing second Chern class.In a second part of the paper, we introduce notions of numerical effectiveness and numerical flatness for principal (Higgs) bundles, discussing their main properties. For (non-Higgs) principal bundles, we show that a numerically flat principal bundle admits a reduction to a Levi factor which has a flat Hermitian–Yang–Mills connection, and, as a consequence, that the cohomology ring of a numerically flat principal bundle with coefficients in R is trivial. To our knowledge this notion of numerical effectiveness is new even in the case of (non-Higgs) principal bundles.     

考虑主应力轴旋转的土体本构关系研究进展

对目前国内外考虑主应力轴旋转的试验研究及本构模型研究进行了总结分析,并对进一步研究提出了相应的建议．基于不同的加载条件,从纯主应力轴旋转和耦合主应力轴旋转两个方面,较全面的描述了主应力轴旋转情况下土体的基本变形特性,并对考虑主应力轴旋转的土体变形试验提出了进一步研究的建议．较为系统地评述了当前较有代表性的考虑主应力轴旋转的土体本构模型（边界面模型、多机构模型、运动硬化模型和广义塑性模型）,得出了广义塑性模型更适合用来描述考虑主应力轴旋转的土体变形特性的结论．总结未来考虑主应力轴旋转的土体本构关系研究的主要方向是:把握主应力轴旋转情况下土体变形的本质特性,建立推理严密、形式简单、适用方便的本构模型,并用来指导工程实践．     

Principal component analysis for histogram-valued data

This paper introduces a principal component methodology for analysing histogram-valued data under the symbolic data domain. Currently, no comparable method exists for this type of data. The proposed method uses a symbolic covariance matrix to determine the principal component space. The resulting observations on principal component space are presented as polytopes for visualization. Numerical representation of the resulting polytopes via histogram-valued output is also presented. The necessary algorithms are included. The technique is illustrated on a weather data set.     

A sufficient condition for the existence of a principal eigenvalue for nonlocal diffusion equations with applications

Considerable work has gone into studying the properties of nonlocal diffusion equations. The existence of a principal eigenvalue has been a significant portion of this work. While there are good results for the existence of a principal eigenvalue equations on a bounded domain, few results exist for unbounded domains. On bounded domains, the Krein–Rutman theorem on Banach spaces is a common tool for showing existence. This article shows that generalized Krein–Rutman can be used on unbounded domains and that the theory of positive operators can serve as a powerful tool in the analysis of nonlocal diffusion equations. In particular, a useful sufficient condition for the existence of a principal eigenvalue is given.     

具有遗传性疾病和性状的遗传位点分析

为便于进行数据分析,首先将数据中的位点信息由原来字母编码方式转换为数值编码的方式,根据位点的编码信息和患病信息,采用Logistic回归的方法,找出某种疾病最有可能的一个或几个致病位点,同时采用显著性检验进一步对建立的模型进行检验,证明了建立结果的合理性。此外,通过主成分分析,从原有的300个主成分中取出了225个主成分尽可能多地反映原来基因变量的信息,再通过主成分Logistic回归分析找出与疾病最有可能相关的一个或几个基因。最后,采用典型相关分析找出与相关性状有关联的基因位点。     

A Penalized Likelihood Approach to Rotation of Principal Components

A new paradigm for enhancing the interpretability of principal components through rotation is presented within the framework of penalized likelihood. The rotated components are computed as the maximizers of a Gaussian-based profile log-likelihood function plus a penalty term defined by a standard rotation criterion. This method enjoys a number of advantages over other methods for principal component rotation, notably (1) the rotation specifically targets ill-defined principal components, which may benefit the most from rotation, and (2) the connection with likelihood allows assessment of the fidelity of the rotated components to the data, thereby guiding the choice of penalty parameter. The method is illustrated with an application to a small functional dataset. Efficient computation of the penalized likelihood solution is possible using recently developed algorithms for optimization under orthogonality constraints.     

Topological grammars for data approximation

A method of topological grammars is proposed for multidimensional data approximation. For data with complex topology we define a principal cubic complex of low dimension and given complexity that gives the best approximation for the dataset. This complex is a generalization of linear and non-linear principal manifolds and includes them as particular cases. The problem of optimal principal complex construction is transformed into a series of minimization problems for quadratic functionals. These quadratic functionals have a physically transparent interpretation in terms of elastic energy. For the energy computation, the whole complex is represented as a system of nodes and springs. Topologically, the principal complex is a product of one-dimensional continuums (represented by graphs), and the grammars describe how these continuums transform during the process of optimal complex construction. This factorization of the whole process onto one-dimensional transformations using minimization of quadratic energy functionals allows us to construct efficient algorithms.     

A principal–agent problem with heterogeneous demand distributions for a carbon capture and storage system

Mechanism design problems optimize contract offerings from a principal to different types of agents who have private information about their demands for a product or a service. We study the implications of uncertainty in agents’ demands on the principal’s contracts. Specifically, we consider the setting where agents’ demands follow heterogeneous distributions and the principal offers a menu of contracts stipulating quantities and transfer payments for each demand distribution. We present analytical solutions for the special case when there are two distributions each taking two discrete values, as well as a method for deriving analytical solutions from numerical solutions. We describe one application of the model in carbon capture and storage systems to demonstrate various types of optimal solutions and to obtain managerial insights.     

On a Type of Fréchet Principal Bundles over Banach Bases

In this paper we study a certain class of Fréchet principal bundles. Those which have structural groups obtained as projective limits of Banach Lie groups. In particular, we prove that each bundle of the previous type can be thought of as a projective limit of Banach principal bundles and any connection of them is a generalized limit of Banach connections. Using the previous, we achieve to translate in the Fréchet case basic geometric properties known so far only for Banach bundles.     

The modified natural power method for principal component computation

A modified version of the natural power method (NP) for fast estimation and tracking of the principal eigenvectors of a vector sequence is Presented. It is an extension of the natural power method because it is a solution to obtain the principal eigenvectors and not only for tracking of the principal subspace. As compared with some power-based methods such as Oja method, the projection approximation subspace tracking (PAST) method, and the novel information criterion (NIC) method, the modified natural power method (MNP) has the fastest convergence rate and can be easily implemented with only O(np) flops of computation at each iteration, where n is the dimension of the vector sequence and p is the dimension of the principal subspace or the number of the principal eigenvectors. Furthermore, it is guaranteed to be globally and exponentially convergent in contrast with some non-power-based methods such as MALASE and OPERA. Selected from Journal of Fudan University (Natural Science), 2004, 43(3): 275–284