Relative ageing of series and parallel systems: Effects of dependence and heterogeneity among components

The effect of system structure on relative ageing properties of coherent systems has been extensively studied in terms of the increasing [reversed] hazard ratio. In this paper, we investigate the effects of dependence and heterogeneity among components on the relative ageing of series and parallel systems. Numerical examples are provided as illustrations.     

Generalized discrete autoregressive moving‐average models

This article proposes the generalized discrete autoregressive moving‐average (GDARMA) model as a parsimonious and universally applicable approach for stationary univariate or multivariate time series. The GDARMA model can be applied to any type of quantitative time series. It allows to compute moment properties in a unique way, and it exhibits the autocorrelation structure of the traditional ARMA model. This great flexibility is obtained by using data‐specific variation operators, which is illustrated for the most common types of time series data, such as counts, integers, reals, and compositional data. The practical potential of the GDARMA approach is demonstrated by considering a time series of integers regarding votes for a change of the interest rate, and a time series of compositional data regarding television market shares.     

基于三类模型的四大银行股票收益率预测研究

运用时间序列分析的预测方法,对四大银行的股票日对数收益率序列进行拟合与预测分析,分别构建ARMA模型、GARCH模型以及ARMA-GARCH组合模型,通过模型比较,实证分析表明:在拟合效果上,ARMA-GARCH模型的拟合优度优于ARMA模型和GARCH模型;在预测效果上,ARMA模型的预测效果最优,ARMA-GARCH模型次之.     

半相依部分线性可加回归模型的统计推断

受实际问题研究的启发, 为减少模型偏差, 提出了一类半相依部分线性可加的半参数回归模型. 这类半相依模型中, 响应变量与 一部分解释变量之间的关系是线性的, 与另一部分解释变量之间的关系未知但具有可加结构, 各方程的误差之间是相关的. 将级 数逼近法、最小二乘法和同期相关的估计结合起来, 提出了用于估计模型参数分量的加权半参数最小二乘估计量(WSLSEs), 和用于估 计模型非参数分量的加权级数逼近估计量(WSEs). 证明了这些加权的估计量比相应的不加权的估计量渐近有效, 并导出了相应的渐近正态性. 另外, 还讨论了利用这些估计量的渐近性质来对模型的参数及非参数分量作统计推断. 用大量的模拟实验考察 了所提出的方法在有限样本情况下的表现, 并对美国的一个关于妇女工资问题的全国纵向调查(NLS)数据集进行了统计分析.     

On Double Stratonovich Fractional Integrals and Some Strong and Weak Approximations

Abstract

Double Stratonovich integrals with respect to the odd part and even part of the fractional Brownian motion are constructed. The first and the second moments of such integrals are explicitly identified. As application of double Stratonovich integrals a strong law of large numbers for efBm and ofBm is derived.

Riemann–Stieltjes integral approximations to double Stratonovich fractional integrals are also considered. The strong convergence (almost surely and mean square) is obtained for approximations based on explicit series expansions of the fractional Brownian processes. The weak convergence is derived for approximations by processes with absolutely continuous paths which converge weakly to the considered fractional Brownian processes. The above-mentioned convergences are obtained for deterministic integrands which are given by bimeasures.     

Long Pulse Operation of the THz Gyrotron with a Pulse Magnet

Long pulse operation up to 1 msec of a high frequency gyrotron with a pulse magnet has been successfully carried out in a frequency range including 1 THz. In the experiments, the timing of an electron beam pulse injection is adjusted at the top of the magnetic field pulse, where the variation of field intensity is negligible. The operation cavity modes seem to be TE_{1, 12} and TE_4,12 at the second harmonics. The corresponding frequencies are 903 GHz and 1,013 GHz, respectively. Additionally several features of radiation measurement results of the gyrotron are described and brief considerations are presented.     

新组合模型在光电功率预测中的应用

为提高光伏预测要求的精准性,文章提出一种新算法将神经网络和ARMA算法改进组合,构成NEW ARMA-BP模型算法.以某30兆瓦的光伏电站采集的输出功率为输入样本,基于ARMA和BP神经网络算法在Matlab环境下依次搭建了相应的预测模型,预估光伏短期输出量.采用"误差正态检验图"判断基于两种不同算法的误差水平,依据两种单模型预测误差,运用所提出的新方法计算权值并获得新的预测值.基于Matlab的仿真结论验证了组合预测在光伏输出预测领域的适用性.     

基于多尺度自适应阶ARMA的混沌序列多步预测

混沌时间序列在自然界以及人们的生产生活中很常见,混沌序列看似杂乱无章但相较于纯随机序列其中蕴含着一些非线性的运动特征,提出一种基于多尺度自适应阶ARMA的混沌时间序列多步预测方法.首先利用自适应噪声的完备经验模态分解(CEEMDAN)对原始混沌序列进行分解,获得不同尺度的固有模态分量(IMF)和残余分量.然后采用经粒子群算法(PSO)进行阶数寻优的自回归移动平均模型(ARMA)对每一个IMF分量进行拟合预测.最后将预测得到的每一个分量相加得到原始混沌序列的预测值.基于Mackay-Glass混沌序列和太阳黑子数混沌序列进行实验分析,实验表明:与ARMA、PSO-ARMA以及CEEMDAN-ARMA方法相比,方法的预测效果有较好的提高,其平均绝对误差(MAE)、均方根误差(RMSE)以及平均绝对百分比误差(MAPE)都有降低.