Oracle GMM estimation for misspecified models via thresholding

We propose a thresholding generalized method of moments (GMM) estimator for misspecified time series moment condition models. This estimator has the following oracle property: its asymptotic behavior is the same as of any efficient GMM estimator obtained under the a priori information that the true model were known. We propose data adaptive selection methods for thresholding parameter using multiple testing procedures. We determine the limiting null distributions of classical parameter tests and show the consistency of the corresponding block-bootstrap tests used in conjunction with thresholding GMM inference. We present the results of a simulation study for a misspecified instrumental variable regression model and for a vector autoregressive model with measurement error. We illustrate an application of the proposed methodology to data analysis of a real-world dataset.     

A new financial stress index model based on support vector regression and control chart

Financial stress index (FSI) is considered to be an important risk management tool to quantify financial vulnerabilities. This paper proposes a new framework based on a hybrid classifier model that integrates rough set theory (RST), FSI, support vector regression (SVR) and a control chart to identify stressed periods. First, the RST method is applied to select variables. The outputs are used as input data for FSI–SVR computation. Empirical analysis is conducted based on monthly FSI of the Federal Reserve Bank of Saint Louis from January 1992 to June 2011. A comparison study is performed between FSI based on the principal component analysis and FSI–SVR. A control chart based on FSI–SVR and extreme value theory is proposed to identify the extremely stressed periods. Our approach identified different stressed periods including internet bubble, subprime crisis and actual financial stress episodes, along with the calmest periods, agreeing with those given by Federal Reserve System reports.     

Local composite quantile regression estimation of time-varying parameter vector for multidimensional time-inhomogeneous diffusion models

This paper is dedicated to the study of the composite quantile regression (CQR) estimations of time-varying parameter vectors for multidimensional diffusion models. Based on the local linear fitting for parameter vectors, we propose the local linear CQR estimations of the drift parameter vectors, and verify their asymptotic biases, asymptotic variances and asymptotic normality. Moreover, we discuss the asymptotic relative efficiency (ARE) of the local linear CQR estimations with respect to the local linear least-squares estimations. We obtain that the local estimations that we proposed are much more efficient than the local linear least-squares estimations. Simulation studies are constructed to show the performance of the estimations proposed.     

Information geodesics for gamma models of communication clustering

Recently, Akyildiz called for further work on non-Poisson models for communication arrivals in distributed networks such as cellular phone systems. The basic ‘random’ model for stochastic events is the Poisson process; for events on a line this resuits in an exponential disiribuuon of intervals between events. Network designers and managers need too monotor and quantify call clustering in order to optimize resaurce usage; the natural reference state from which to measure departures is that arising from a Poisson, process of calls. Here we consider gamma distributions, which contain exponential distributions as a special case. The surface representing gamma models has a natural Riemannian information metric and we obtain some geodesic sprays for this metric. The exponential distributions form a 1-dimensional subspace of the 2-dimensional space of all gamma distributions, so we have an isometric embedding of the random model as a subspace of the gamma models. This geometry may provide an appropriate structure on which to represent clustering as quantifiable departures from randomness and on which to impose dynamic control algorithms to optimize traffic at receiving nodes in distributed communication networks. In practice, we may expect correlation between call arrival times and call duration, reflecting for example peaks of different users of internet services. This would give rise to a twisted product of two surfaces with the twisting controlled by the correlation. Though bivariate gamma models do exist, such as Kibble's, none has tractabie information geometry nor sufficiently general marginal gammas,but a simulation method of approach is suggested.     

A high breakdown,high efficiency and bounded influence modified GM estimator based on support vector regression

Regression analysis aims to estimate the approximate relationship between the response variable and the explanatory variables. This can be done using classical methods such as ordinary least squares. Unfortunately, these methods are very sensitive to anomalous points, often called outliers, in the data set. The main contribution of this article is to propose a new version of the Generalized M-estimator that provides good resistance against vertical outliers and bad leverage points. The advantage of this method over the existing methods is that it does not minimize the weight of the good leverage points, and this increases the efficiency of this estimator. To achieve this goal, the fixed parameters support vector regression technique is used to identify and minimize the weight of outliers and bad leverage points. The effectiveness of the proposed estimator is investigated using real and simulated data sets.     

再生核支持向量机在非线性系统中的应用

为了提高非线性系统辨识的精度,提出用Walsh函数作为空间V0的尺度函数,构造出L2(R)空间的正交规范序列。结合小波多分辨分析,将Hilbert空间分为一系列子空间,并由可分Hilbert空间与L2(R)的等价性,利用内积同构的线性算子,可以把V0子空间的尺度函数折算为Hilbert空间的子空间V0的尺度函数,构造出新的Walsh序列再生核。通过仿真实验,与传统的RBF核函数、高斯核函数等比较,该尺度再生核函数具有更高的辨识精度,较少支持向量数目,充分体现了支持向量机较好的推广性能。     

射频六端口OFDM解调器研究

该文提出了一种新的基于射频六端口网络的多频带OFDM解调器。推导了该解调器的数学模型,仿真了用于WLAN802.11a通信系统时,射频六端口网络的本振电平、输入信号电平对解调器性能的影响。研究了射频六端口网络中正交电桥和同相电桥分别存在相位幅度不平衡时和同时存在相位幅度不平衡时解调器误差矢量幅度的特性。讨论了解调器的动态范围、接收性能等指标。结果表明结合当前的MMIC集成水平,使用射频六端口网络可以实现多频带、多制式、多载波的OFDM解调器。     

B股与H股及红筹股之间的溢出效应与信息流动

运用向量GARCH-M模型检验了B股与H股及红筹股之间的溢出效应与信息流动.实证结果表明:红筹股对沪深市B股、H股对沪市B股的收益和波动溢出效应均显著存在,而反向的溢出效应均不显著,表明信息是从红筹股向沪深市B股、从H股向沪市B股单向流动的;深市B股与H股之间相互的收益溢出效应均存在,而波动溢出效应均不存在,信息在H股和深市B股之间的流动情况不明显;在信息流动过程中,红筹股始终处于信息领先地位.     

基于Shapley值法的移动增值服务价值链利润分配模型

研究对象为任意节点连接和任意支撑的平面框架。一般梁单元由等截面直杆及其杆端的轴向弹簧、切向弹簧和转动弹簧组成,推导得到此类单元的刚度矩阵、单元在8种基本荷载作用下的等效节点荷载。采用Matlab语言编写了适用于一般节点非线性连接框架的静力分析程序,非线性形式为指数函数或多项式函数,可以得到结构不同连接刚度下的节点位移、杆端位移和杆端力。算例显示出节点柔度对结构受力和变形的影响。     

基于支持向量机的传感器非线性误差校正

提出了一种用支持向量机校正传感器非线性误差的原理和方法。该算法只依据样本就可以正确辩识传感器逆模型特征,而不需关于逆模型函数形式的任何先验知识,并将原问题转化为一个凸二次优化问题,能够保证找到的极值解就是全局最优解,具有较好的泛化能力。通过对电容式湿敏传感器误差校正的应用表明:该算法可取得较好的效果。