半相依回归系统参数的c-k型改进估计

针对设计矩阵Xi呈病态时的半相依线性回归系统,提出了系统参数iβ的一种c-k型改进估计,并证明了这种估计在均方误差意义下的若干优良性质.     

土的地震液化概率评估模型

基于278个土的地震液化或非液化实测数据,运用二元回归方法推导出两个对数模型.这两个模型将液化概率表示成地震荷载和土阻力参数的函数,以修正的标准贯入锤击数来量度土的抗液化能力.文中将所提出的对数模型与确定性液化判别标准进行了比较,并将模型应用于典型场地的分析.结果表明,所提出的两个对数模型能客观地评价数据中的相关信息,可直接量化基于统计数据的条件液化概率.     

乘方权重不等式的证明及其系列推论

给出了乘方权重不等式的一种证明方法,并由此得到一系列重要推论.该系列推论在证明不等式及求极限过程中有着广泛的应用.     

EWMA控制图在MSA稳定性分析中的应用

针对传统的 Shewhart 控制图使用当前观测数据进行测量系统分析(MSA)的稳定性分析,不易检测出过程均值中存在的小偏移,而指数加权滑动平均(EWMA)和累计和(CUSUM)控制图考虑了历史数据,可以克服Shewhart 控制图在小波动情况下检测能力不足的缺陷:首先通过 Montle-Carlo 模拟计算控制图性能评价指标ARL(average run length),分析了 EWMA 和 CUSUM 控制图的优越性;然后给出了 EWMA 控制图在 MSA 稳定性分析中的实施步骤.示例分析表明 EWMA 在小波动时比 CUSUM 和 Shewhart 控制图更为有效.     

基于加权最小二乘拟蒙特卡罗的美式期权定价

美式期权定价具有后向迭代搜索特征, 在最小二乘拟蒙特卡罗模拟(least-squares quasi-Monte Carlo, LSM)方法的基础上, 本文通过随机Faure序列以及对偶变数法增加抽样数目, 达到减小模拟方差的目的,用其计算标的资产价格, 然后用加权最小二乘法进行回归, 得到了加权最小二乘拟蒙特卡罗(weighted least-squares quasi-Monte Carlo, WLSQM)方法. 从期权价值、标准差、运行时间几个方面比较方法的优劣, 得出WLSQM 方法比LSM 方法估计效果更优的结果, 验证了WLSQM方法在美式期权定价上的有效性.     

PAM对土壤蒸发的影响分析及其模拟研究

在栽种与不栽种作物的情况下,分别对3种处理的土壤蒸发进行抑制性及差异性分析,并结合气象因子对无作物的土壤累积蒸发量进行多元线性回归分析.结果表明,在无作物的情况下,随着PAM用量的增加,PAM对土壤蒸发抑制效果明显,3种处理两两差异显著,但未达到极显著水平;有作物的情况下,PAM对土壤蒸发抑制效果不明显,但3种处理间的差异性均达到极显著水平.模型的拟合结果表明,3种处理的土壤累积蒸发量均可用多元线性回归模型拟合,且拟合效果较好.图2,表5,参8.     

我国通货膨胀的混合回归和时间序列模型

回归模型的残差项反映了对被解释变量有影响但未列入解释变量的因素所产生的噪音 ,这部分噪音可由时间序列模型进行拟合 .本文对通货膨胀建立了一个混合回归和时间序列模型 ,并将该模型的预测结果与单纯用回归模型的预测结果进行了比较.     

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.     

The Information Content of Equity Block Trades on the Warsaw Stock Exchange: Conventional and Bootstrap Approaches

This paper focuses on the Polish stock market by analysing the information content of 95 equity block trade transactions executed on shares of companies constituting the WIG20 index. A normalized conventional approach and a bootstrap approach are used to draw inferences. These approaches make use of a multivariate regression model with two explanatory variables: a market return and a dummy variable for the event. Resampling allows construction of an empirical distribution of the normalized test statistic. The outcomes obtained from the application of a normalized conventional approach as well as a bootstrap approach are in line and confirm that equity block trade transactions carry an important signal to investors. Significant abnormal positive (negative) returns are associated with the execution of the equity block trades, the prices of which are higher (lower) than the closing prices 2 days before the execution of the equity block trade transactions. Copyright © 2015 John Wiley & Sons, Ltd.     

Dynamic Latent Class Model Averaging for Online Prediction

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state‐space model for the parameters of each of the candidate models of the system with a Markov chain model for the best model. We propose a polychotomous regression model for the transition weights to assume that the probability of a change in time depends on the past through the values of the most recent time periods and spatial correlation among the regions. The evolution of the parameters in each submodel is defined by exponential forgetting. This structure allows the ‘correct’ model to vary over both time and regions. In contrast to existing methods, the proposed model naturally incorporates clustering and prediction analysis in a single unified framework. We develop an efficient Gibbs algorithm for computation, and we demonstrate the value of our framework on simulated experiments and on a real‐world problem: forecasting IBM's corporate revenue. Copyright © 2014 John Wiley & Sons, Ltd.