Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?

We study the performance of recently developed linear regression models for interval data when it comes to forecasting the uncertainty surrounding future stock returns. These interval data models use easy‐to‐compute daily return intervals during the modeling, estimation and forecasting stage. They have to stand up to comparable point‐data models of the well‐known capital asset pricing model type—which employ single daily returns based on successive closing prices and might allow for GARCH effects—in a comprehensive out‐of‐sample forecasting competition. The latter comprises roughly 1000 daily observations on all 30 stocks that constitute the DAX, Germany's main stock index, for a period covering both the calm market phase before and the more turbulent times during the recent financial crisis. The interval data models clearly outperform simple random walk benchmarks as well as the point‐data competitors in the great majority of cases. This result does not only hold when one‐day‐ahead forecasts of the conditional variance are considered, but is even more evident when the focus is on forecasting the width or the exact location of the next day's return interval. Regression models based on interval arithmetic thus prove to be a promising alternative to established point‐data volatility forecasting tools. Copyright ©2015 John Wiley & Sons, Ltd.     

充填体强度影响因素及组合预测模型

水灰比、堆积密实度和比表面积是充填体强度的重要影响因素,但有关各因素对强度影响显著性的研究较少,为此通过3因素5水平正交试验,分别对龄期为3 d,7 d和28 d的试块开展单轴抗压强度试验.对试验结果进行方差分析,获得不同龄期各因素F统计值比(水灰比:堆积密实度:比表面积):3d为698.404:26.148:0.91...     

自校正信息融合Kalman预报器

对含未知噪声统计的多传感器系统,用现代时间序列分析方法,基于滑动平均（MA）新息模型的在线辨识和求解相关函数矩阵方程组,可在线估计噪声统计,进而在按矩阵加权线性最小方差最优信息融合准则下,提出了自校正信息融合Kalman预报器。证明了它的收敛性,即它具有渐近最优性,且自校正融合Kalman预报器比每个局部自校正Kalman预报器精度高。一个目标跟踪系统的仿真例子说明了其有效性。     

辨识动态系统噪声方差Q和R的新方法

对于带未知噪声方差的线性离散定常随机系统,引入左素分解可得到一个新的观测过程,它用两个滑动平均（MA）过程之和表示。用解相关函数矩阵方程组得到了噪声方差Q和R的估值器,进而基于新的观测过程的采样相关函数及其遍历性可得到噪声方差Q和R的强一致估计。算法简单,便于实时应用。一个目标跟踪系统的仿真例子说明了其有效性。     

单个总体方差差异的U统计量检验法

给出一种利用非参数统计中U统计量构造检验单个总体方差差异的方法,与χ2检验法相比,该方法不仅适用于更宽泛的场合,而且其渐近相对效率为1.     

目标变量不完全情形下的最小二乘回归插补

充分利用对辅助变量的回答所提供的变量信息，采用最小二乘方法，给出了目标变量不完全情形下对其的一种回归插补，并对回归差补的期望和方差进行了讨论。     

Utilizations of Statistics on Optimizing Parameters in Compact Spinning with Pneumatic Groove

The compact spinning with pneumatic groove utilizes transverse air force and mechanical force of the pneumatic groove to condense fiber bundles to eliminate the spinning triangle.Range analysis and var...     

关于双重抽样

本文指出并改正了[1]中第十二章关于双重抽样的不正确的结论。给出了关于双重抽样的一些结论的严格证明。     

提高OCR识别率的研究

根据ＯＣＲ（ＯｐｔｉｃａｌＣｈａｒａｃｔｅｒＲｅａｄｅｒ）读入图象象素灰度的统计分布关系，利用象素灰度分布的类间方差，找出使象素灰度分布的类间方差为最大的象素灰度级，并以此作为图象的二值化阈值，对由ＯＣＲ读入的帐票图象等进行二值化处理，以解决人工设定阈值法所带来的文字识别率下降的问题．