基于广义Beta回归的不良贷款回收率模型

依据国内最大的违约损失率数据库-LossMet rics~(TAI),利用广义beta回归模型对时间跨度为2001-2008年的不良贷款回收率分布进行了研究。采用极大似然和OLS估计方法,针对全样本和回收率为(0.1)开区间的非极端回收样本给出包含宏观、贷款和债务人异质因子的多变量广义beta回归模型的拟合参数和各个因素对回收率均值、方差的影响分析,并在多种变换方式下研究了只含宏观因子的单变量模型。结果表明,宏观因子的影响在单、多变量模型中都十分显著;不良贷款的有效抵质押和债务人的经营状况对模型的拟合也有明显影响;各因素对全样本和非极端回收样本的拟合结果差异性显著,同一因素对回收率均值、方差的影响大为不同。应用所得广义beta回归模型可进一步讨论回收率的区间估计、在险价值(VaR)等问题,给不良贷款风险管理提供极大帮助。     

中国建筑产业竞争力形成机理分析——基于PLS结构方程模型的实证研究

在对产业竞争力研究现状简单分析的基础上,述评了产业竞争力经典分析范式,提出了一个新的产业竞争力分析框架。在此基础上借鉴因果思想,总结建筑产业竞争力影响因素和度量指标,建立了中国建筑产业竞争力形成机理概念模型;采用PLS结构方程模型对中国2006、2007年各省份(除西藏外)建筑产业的60组基本数据进行了实证分析,验证了模型合理性。研究结果显示区域环境、生产要素、产业组织三个潜变量对当地建筑产业竞争力产生直接的影响作用。最后,进行了不足分析与研究展望。     

基于经验模式分解的三频彩色条纹投影轮廓术

为实现动态物体的实时三维测量,提出了一种基于经验模式分解的三频彩色条纹投影轮廓术。将低、中、高三种频率的正弦条纹分别经投影仪红(R),绿(G),蓝(B)通道同时投影至被测物面,CCD在另一角度拍摄变形条纹图。将变形条纹图R、G、B三分量互减消减背景干扰,用经验模式分解进行颜色解耦,分离各载频项,进而以傅里叶变换解调相位。以变精度去包裹算法按低、中、高频依次完成包裹相位展开,得到高频载频项的展开相位。计算机模拟时相位解调的标准差小于0.0417rad,具有较高的测量精度;对比实验和面部表情变化实验进一步说明了方法的可靠性。该方法在单次拍摄下实现了相位的解调及高精度相位的精确展开,为动态物体的高精度轮廓测量提供了有效的手段。     

Comparison of performance due to guided hyperlearning,unguided hyperlearning,and conventional learning in mathematics: an empirical study

In this paper, the use of guided hyperlearning, unguided hyperlearning, and conventional learning methods in mathematics are compared. The design of the research involved a quasi-experiment with a modified single-factor multiple treatment design comparing the three learning methods, guided hyperlearning, unguided hyperlearning, and conventional learning. The participants were from three first-year university classes, numbering 115 students in total. Each group received guided, unguided, or conventional learning methods in one of the three different topics, namely number systems, functions, and graphing. The students’ academic performance differed according to the type of learning. Evaluation of the three methods revealed that only guided hyperlearning and conventional learning were appropriate methods for the psychomotor aspects of drawing in the graphing topic. There was no significant difference between the methods when learning the cognitive aspects involved in the number systems topic and the functions topic.     

Quantitative Stability Analysis of Two-Stage Stochastic Linear Programs with Full Random Recourse

Abstract

In this paper, we apply the parametric linear programing technique and pseudo metrics to study the quantitative stability of the two-stage stochastic linear programing problem with full random recourse. Under the simultaneous perturbation of the cost vector, coefficient matrix, and right-hand side vector, we first establish the locally Lipschitz continuity of the optimal value function and the boundedness of optimal solutions of parametric linear programs. On the basis of these results, we deduce the locally Lipschitz continuity and the upper bound estimation of the objective function of the two-stage stochastic linear programing problem with full random recourse. Then by adopting different pseudo metrics, we obtain the quantitative stability results of two-stage stochastic linear programs with full random recourse which improve the current results under the partial randomness in the second stage problem. Finally, we apply these stability results to the empirical approximation of the two-stage stochastic programing model, and the rate of convergence is presented.     

Application of maximum likelihood multivariate curve resolution to noisy data sets

In this work, two different maximum likelihood approaches for multivariate curve resolution based on maximum likelihood principal component analysis (MLPCA) and on weighted alternating least squares (WALS) are compared with the standard multivariate curve resolution alternating least squares (MCR‐ALS) method. To illustrate this comparison, three different experimental data sets are used: the first one is an environmental aerosol source apportionment; the second is a time‐course DNA microarray, and the third one is an ultrafast absorption spectroscopy. Error structures of the first two data sets were heteroscedastic and uncorrelated, and the difference between them was in the existence of missing values in the second case. In the third data set about ultrafast spectroscopy, error correlation between the values at different wavelengths is present. The obtained results confirmed that the resolved component profiles obtained by MLPCA‐MCR‐ALS are practically identical to those obtained by MCR‐WALS and that they can differ from those resolved by ordinary MCR‐ALS, especially in the case of high noise. It is shown that methods that incorporate uncertainty estimations (such as MLPCA‐ALS and MCR‐WALS) can provide more reliable results and better estimated parameters than unweighted approaches (such as MCR‐ALS) in the case of the presence of high amounts of noise. The possible advantage of using MLPCA‐MCR‐ALS over MCR‐WALS is then that the former does not require changing the traditional MCR‐ALS algorithm because MLPCA is only used as a preliminary data pretreatment before MCR analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

Nonlinear mixture‐wise expansion approach to underdetermined blind separation of nonnegative dependent sources

Underdetermined blind separation of nonnegative dependent sources consists in decomposing a set of observed mixed signals into greater number of original nonnegative and dependent component (source) signals. That is an important problem for which very few algorithms exist. It is also practically relevant for contemporary metabolic profiling of biological samples, such as biomarker identification studies, where sources (a.k.a. pure components or analytes) are aimed to be extracted from mass spectra of complex multicomponent mixtures. This paper presents a method for underdetermined blind separation of nonnegative dependent sources. The method performs nonlinear mixture‐wise mapping of observed data in high‐dimensional reproducible kernel Hilbert space (RKHS) of functions and sparseness‐constrained nonnegative matrix factorization (NMF) therein. Thus, the original problem is converted into new one with increased number of mixtures, increased number of dependent sources, and higher‐order (error) terms generated by nonlinear mapping. Provided that amplitudes of original components are sparsely distributed, which is the case for mass spectra of analytes, sparseness‐constrained NMF in RKHS yields, with significant probability, improved accuracy relative to the case when the same NMF algorithm is performed on the original problem. The method is exemplified on numerical and experimental examples related respectively to extraction of 10 dependent components from five mixtures and to extraction of 10 dependent analytes from mass spectra of two to five mixtures. Thereby, analytes mimic complexity of components expected to be found in biological samples. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling electricity spot prices using mean-reverting multifractal processes

We discuss stochastic modeling of volatility persistence and anti-correlations in electricity spot prices, and for this purpose we present two mean-reverting versions of the multifractal random walk (MRW). In the first model the anti-correlations are modeled in the same way as in an Ornstein–Uhlenbeck process, i.e. via a drift (damping) term, and in the second model the anti-correlations are included by letting the innovations in the MRW model be fractional Gaussian noise with H<1/2$H<1/2$. For both models we present approximate maximum likelihood methods, and we apply these methods to estimate the parameters for the spot prices in the Nordic electricity market. The maximum likelihood estimates show that electricity spot prices are characterized by scaling exponents that are significantly different from the corresponding exponents in stock markets, confirming the exceptional nature of the electricity market. In order to compare the damped MRW model with the fractional MRW model we use ensemble simulations and wavelet-based variograms, and we observe that certain features of the spot prices are better described by the damped MRW model. The characteristic correlation time is estimated to approximately half a year.     

基于聚合经验模态分解方法的OJ 287 射电流量变化周期分析

BL Lac 天体OJ 287 是一个重要的Blazar天体, 光变具有准周期特点. 本文从密歇根大学射电天文台数据库收集了OJ 287射电波段4.8, 8.0和 14.5 GHz超过30年时间的观测数据. 由于天文观测资料的复杂性, 一些传统寻找周期的方法不是太理想. 近几年迅速发展起来的聚合经验模态分解(EEMD) 特别适合于具有非线性和非平稳动态变化特性的Blazar天体光变规律的研究. 运用EEMD方法对OJ 287 的3个射电波段流量分别进行多时间尺度分解, 各获得6个代表不同时间尺度局部特征信息的本征模态函数分量和一个趋势项. 研究结果表明: 4.8 GHz射电流量变化的主要周期是18.9, 11.9, 5.7 和2.4年; 8.0 GHz 的主要周期是12.2, 5.2和2.4 年; 14.5 GHz的主要周期是21.8, 12.0, 4.3和 2.4 年. 尽管3个波段流量变化主要周期大小有一定的差别, 但是存在着近似相同的变化周期12.0 和2.4年, 说明这3个波段可能来源于相同的辐射机制, 也证实了OJ 287 射电波段和光学波段具有相同的长光变周期12.0 年. 这些结论表明: EEMD 作为一种全新的非线性、非平稳信号处理方法, 可以更精确地提取Blazar天体中不同光变周期的分量. 关键词： OJ 287 光变周期 聚合经验模态分解