指数自回归模型的辨识的研究

本文讨论了指数自回归模型的辨识问题，证明了该模型的最小二乘问题的非凸性，并给出其保证凸性的条件，最后运用混合算法，辨识了该模型，并用数值算例加以说明。     

Autoregressive gamma processes

We introduce a class of autoregressive gamma processes with conditional distributions from the family of noncentred gamma (up to a scale factor). The paper provides the stationarity and ergodicity conditions for ARG processes of any autoregressive order p, including long memory, and closed‐form expressions of conditional moments. The nonlinear state space representation of an ARG process is used to derive the filtering, smoothing and forecasting algorithms. The paper also presents estimation and inference methods, illustrated by an application to interquote durations data on an infrequently traded stock listed on the Toronto Stock Exchange (TSX). Copyright © 2006 John Wiley & Sons, Ltd.     

Bias in the estimation of non‐linear transformations of the integrated variance of returns

Volatility models such as GARCH, although misspecified with respect to the data‐generating process, may well generate volatility forecasts that are unconditionally unbiased. In other words, they generate variance forecasts that, on average, are equal to the integrated variance. However, many applications in finance require a measure of return volatility that is a non‐linear function of the variance of returns, rather than of the variance itself. Even if a volatility model generates forecasts of the integrated variance that are unbiased, non‐linear transformations of these forecasts will be biased estimators of the same non‐linear transformations of the integrated variance because of Jensen's inequality. In this paper, we derive an analytical approximation for the unconditional bias of estimators of non‐linear transformations of the integrated variance. This bias is a function of the volatility of the forecast variance and the volatility of the integrated variance, and depends on the concavity of the non‐linear transformation. In order to estimate the volatility of the unobserved integrated variance, we employ recent results from the realized volatility literature. As an illustration, we estimate the unconditional bias for both in‐sample and out‐of‐sample forecasts of three non‐linear transformations of the integrated standard deviation of returns for three exchange rate return series, where a GARCH(1, 1) model is used to forecast the integrated variance. Our estimation results suggest that, in practice, the bias can be substantial. Copyright © 2006 John Wiley & Sons, Ltd.     

电流变传动器AR三谱特性

应用新的技术方法研究电流变传动器的非高斯过程.高阶谱分析是一种新的信号处理的有效技术,它可以定量描写信号间的非线性耦合,可以抑制噪声、保留了信号的相位信息.本研究当零均值的非高斯白噪声干扰电流变传动器时,利用输出的采样数据,建立四阶累积量的时间序列的自回归模型和自回归三谱,用于分析电流变传动器的动态特性.电流变的传动器的自动检测与控制系统是用NI虚拟仪器构成的.     

基于能量特征的左右手运动想象脑信号的识别方法

提出了一种基于能量特征的左右手运动想象识别方法,利用快速傅里叶变换分析特定脑电(μ波和β波)的频率分布,然后利用小波分解去噪,再利用小波包分析脑电能量,提取能量特征,最后基于支持向量机(SVM)进行左右手运动想象的识别。本文把能量作为特征的支持向量机(SVM)识别法分别与自适应自回归系数法(AAR)和相同步分析法进行比较。仿真结果表明:在相同样本数据情况下,能量特征作为特征向量的SVM识别准确率明显高于其他2种方法。     

带相依利率结构风险模型的破产持续时间

在利率具有二阶自回归相依结构,同时考虑到保费、理赔支付时间的离散时间风险模型下,得到了在停时T,保险公司在初始准备金为u时,破产持续时间的分布的递推公式.     

基于数据列变换的自回归预测方法的改善

证明了利用反双曲正弦函数变换能提高数据列的光滑程度,获得结论:设｛x_(k)｝为递增数据列,x_(1)>0,y(k)=ln(x_(k) (x_(k)~2 1)~(1/2)),则数据列｛y_(k)｝比数据列｛x_(k)｝光滑.给出了改善的自回归预测方法,并且举例加以论证.     

具有相依利息率的离散时间保险风险模型的一个联合分布

研究了在利率具有一阶自回归结构的情况下的离散时间保险风险模型,得到了破产前最大盈余的分布以及破产前盈余,破产后赤字与破产前最大盈余的联合分布的递推公式及其积分方程.     

Convergence of Recursive Identification for ARMAX Process with Increasing Variances

The autoregressive moving average exogenous (ARMAX) model is commonly adopted for describing linear stochastic systems driven by colored noise. The model is a finite mixture with the ARMA component and external inputs. In this paper we focus on a parameter estimate of the ARMAX model. Classical modeling methods are usually based on the assumption that the driven noise in the moving average (MA) part has bounded variances, while in the model considered here the variances of noise may increase by a power of log n. The plant parameters are identified by the recursive stochastic gradient algorithm. The diminishing excitation technique and some results of martingale difference theory are adopted in order to prove the convergence of the identification. Finally, some simulations are given to show the reliability of the theoretical results.     

基于LabVIEW的造气炉温度辨识系统的研究

通过对造气炉的结构特点和反应机理分析,以及对测温干扰的估计,采用带受控项自回归滑动平均模型(CARMA)来表征其温度特性较为理想,但这种算法复杂,较难实现。基于任何CARMA模型都可以用高阶带受控项的自回归模型(CAR)逼近到任何精度,实现起来却又相对简单的特点,以LabVIEW为平台来完成对气化层温度的任意高阶CAR建模,能够很好的解决气化层温度难以直接测量的问题。