On the Predictive Content of Autoregression Residuals: A Semiparametric,Copula‐Based Approach to Time Series Prediction

This paper proposes an adjustment of linear autoregressive conditional mean forecasts that exploits the predictive content of uncorrelated model residuals. The adjustment is motivated by non‐Gaussian characteristics of model residuals, and implemented in a semiparametric fashion by means of conditional moments of simulated bivariate distributions. A pseudo ex ante forecasting comparison is conducted for a set of 494 macroeconomic time series recently collected by Dees et al. (Journal of Applied Econometrics 2007; 22: 1–38). In total, 10,374 time series realizations are contrasted against competing short‐, medium‐ and longer‐term purely autoregressive and adjusted predictors. With regard to all forecast horizons, the adjusted predictions consistently outperform conditionally Gaussian forecasts according to cross‐sectional mean group evaluation of absolute forecast errors and directional accuracy. Copyright © 2012 John Wiley & Sons, Ltd.     

Backward‐in‐Time Selection of the Order of Dynamic Regression Prediction Model

We investigate the optimal structure of dynamic regression models used in multivariate time series prediction and propose a scheme to form the lagged variable structure called Backward‐in‐Time Selection (BTS), which takes into account feedback and multicollinearity, often present in multivariate time series. We compare BTS to other known methods, also in conjunction with regularization techniques used for the estimation of model parameters, namely principal components, partial least squares and ridge regression estimation. The predictive efficiency of the different models is assessed by means of Monte Carlo simulations for different settings of feedback and multicollinearity. The results show that BTS has consistently good prediction performance, while other popular methods have varying and often inferior performance. The prediction performance of BTS was also found the best when tested on human electroencephalograms of an epileptic seizure, and for the prediction of returns of indices of world financial markets.Copyright © 2013 John Wiley & Sons, Ltd.     

协同多点传输中迫零预编码和天线选择技术

MIMO无线传输技术极大地提高了系统的容量,在实际通信系统中,整个网络是一个干扰受限的系统,小区间干扰对MIMO系统传输的影响是显著的,每个相邻小区的基站天线都可以看成一个干扰源．由于基站端数据处理能力的提升和回程容量的增加,多个小区协同多点传输技术引起了人们的广泛关注．目前的大部分工作都是集中于研究系统的容量（吞吐量）,而在实际系统中,每个用户的接收等效信噪比（即公平性）对系统的性能如误帧率等有重要的影响．对于每个用户为单天线的情形,研究了总功率受限和每天线功率受限下采用迫零预编码的系统容量和公平性．对两种功率约束条件下的公平性进行了分析,得到了公平性算法的闭式表达式．当每个用户为多天线的情形,为了降低计算的复杂度,引入了信道范数最大的接收天线选择算法,把每个用户为多天线的情形转化成等效的每个用户单天线情形,推导的每用户为单天线的公平性算法仍然适用．仿真结果显示,采用迫零预编码的多小区协作可以使系统性能显著提升．在相同的迫零预编码下,不同的功率分配策略对系统的容量和公平性有显著的影响．和用户为单天线相比,采用天线选择算法可以提升系统的容量和公平性．考虑了用户公平性时的吞吐量和最大系统吞吐量之间的折中关系,并给出了仿真结果．     

模糊综合评判法及其在人才选拔中的应用

将模糊数学的理论方法应用于学生能力的综合评判中,提出了一种人才能力量化的评判方法.通过实例说明该方法的可行性,为人才招聘与人才选拔提供一种新的方法.     

市盈率变动与盈余管理的关系

分析盈余管理的涵义,从契约论与信息经济学2个角度诠释盈余管理产生的理论基础;借助Edwards-Bell-Ohlson（EBO）模型阐述市盈率P/E的涵义及其与公司业绩的关系;从市盈率变动、公司成长性以及公司价值可能被错误评价角度,分析市盈率变动与盈余管理的关系,提出防范盈余管理的建议.     

计算机生成兵力的目标选择研究

根据CGF目标选择的模糊性特点,介绍了一个基于模糊集理论的目标威胁分析算法和基于效用理论的目标选择算法。基于模糊集理论的威胁分析能准确反映和描述实际战场环境下信息的模糊特性,算法通过隶属度函数计算得出不同威胁因素的隶属度值,然后通过层次分析法综合各威胁因素隶属度值获得每个目标的威胁程度。基于效用理论的目标选择算法通过效用函数综合考虑了目标的威胁程度和与之交战的期望效益两方面因素,能很好地模拟实际作战兵力的目标选择过程。     

一种基于SVD的射频收发天线选择算法

基于线性检测的空间复用(SM)系统提出了一种射频天线选择算法,该算法以最小化错误概率为目标,利用信道矩阵的奇异值分解(SVD)所得到的左奇异和右奇异矢量实现发端和收端的射频天线选择,且不需要改变系统原有的检测算法。该算法可以用于收发任一端或两端同时使用,不采用遍历搜索,与传统搜索算法相比实现复杂度低。仿真证明将该算法应用于链路任意一端相比与传统算法都可以获得更多的分集增益和阵列增益。     

自适应小生境克隆选择算法及其仿真研究

为了对多模态函数寻优,基于免疫克隆选择算法原理,提出了自适应小生境克隆选择算法(ANCSA).小生境决定位段会随着优化对象的维数及可行域的变化而自动调节,从而形成不同的小生境,每个小生境都具有免疫记忆功能.通过对四个典型的多模态函数仿真,并和相关算法进行比较分析,结果表明ANCSA在解决多模态函数优化问题时具有较强的自适应性和收敛性.     

异构无线分组网络中的接入选择机制

传统多接入选择(Multi-Radio Access Selection)方法只针对话音业务,在设计中仅仅考虑已接入用户数及系统容量,由于未将"分组级"信息考虑在内而不适用于无线分组网络。提出了一种新的异构无线分组网络中的接入选择算法,分组业务在多接入选择时考虑了实时、非实时的不同QoS要求和链路自适应机制(链路层重传及自适应调制编码);用户接入后,在保证其QoS的基础上,同时达到不同网络间负载均衡的目的。基于OPNET Modeler的动态仿真对算法性能进行了仿真评估,并与传统的最小用户数和最小路径损耗多接入选择方法在保证业务QoS和负载均衡性能方面进行了比较。     

Improving moving average trading rules with boosting and statistical learning methods

We present a system for combining the different types of predictions given by a wide category of mechanical trading rules through statistical learning methods (boosting, and several model averaging methods like Bayesian or simple averaging methods). Statistical learning methods supply better out‐of‐sample results than most of the single moving average rules in the NYSE Composite Index from January 1993 to December 2002. Moreover, using a filter to reduce trading frequency, the filtered boosting model produces a technical strategy which, although it is not able to overcome the returns of the buy‐and‐hold (B&H) strategy during rising periods, it does overcome the B&H during falling periods and is able to absorb a considerable part of falls in the market. Copyright © 2008 John Wiley & Sons, Ltd.