Additive hazards regression with random effects for clustered failure times

Additive hazards model with random effects is proposed for modelling the correlated failure time data when focus is on comparing the failure times within clusters and on estimating the correlation between failure times from the same cluster, as well as the marginal regression parameters. Our model features that, when marginalized over the random effect variable, it still enjoys the structure of the additive hazards model. We develop the estimating equations for inferring the regression parameters. The proposed estimators are shown to be consistent and asymptotically normal under appropriate regularity conditions. Furthermore, the estimator of the baseline hazards function is proposed and its asymptotic properties are also established. We propose a class of diagnostic methods to assess the overall fitting adequacy of the additive hazards model with random effects. We conduct simulation studies to evaluate the finite sample behaviors of the proposed estimators in various scenarios. Analysis of the Diabetic Retinopathy Study is provided as an illustration for the proposed method.     

Estimation of rating classes and default probabilities in credit risk models with dependencies

Let Y = m(X) + ε be a regression model with a dichotomous output Y and a one‐step regression function m . In the literature, estimators for the three parameters of m , that is, the breakpoint θ and the levels a and b , are proposed for independent and identically distributed (i.i.d.) observations. We show that these standard estimators also work in a non‐i.i.d. framework, that is, that they are strongly consistent under mild conditions. For that purpose, we use a linear one‐factor model for the input X and a Bernoulli mixture model for the output Y . The estimators for the split point and the risk levels are applied to a problem arising in credit rating systems. In particular, we divide the range of individuals' creditworthiness into two groups. The first group has a higher probability of default and the second group has a lower one. We also stress connections between the standard estimator for the cutoff θ and concepts prevalent in credit risk modeling, for example, receiver operating characteristic. Copyright © 2014 John Wiley & Sons, Ltd.     

Numerical discretization-based kernel type estimation methods for ordinary differential equation models

We consider the problem of parameter estimation in both linear and nonlinear ordinary differential equation(ODE) models. Nonlinear ODE models are widely used in applications. But their analytic solutions are usually not available. Thus regular methods usually depend on repetitive use of numerical solutions which bring huge computational cost. We proposed a new two-stage approach which includes a smoothing method(kernel smoothing or local polynomial fitting) in the first stage, and a numerical discretization method(Eulers discretization method, the trapezoidal discretization method,or the Runge–Kutta discretization method) in the second stage. Through numerical simulations, we find the proposed method gains a proper balance between estimation accuracy and computational cost.Asymptotic properties are also presented, which show the consistency and asymptotic normality of estimators under some mild conditions. The proposed method is compared to existing methods in term of accuracy and computational cost. The simulation results show that the estimators with local linear smoothing in the first stage and trapezoidal discretization in the second stage have the lowest average relative errors. We apply the proposed method to HIV dynamics data to illustrate the practicability of the estimator.     

基于AltiVec技术的PowerPC处理器矢量运算性能测试

基于AltiVec技术的PowerPC处理器,在很多嵌入式信号处理领域已经取代传统的DSP处理器成为信号处理器件的首选。为了评估基于AltiVec技术的PowerPC处理器的矢量运算性能和信号处理能力,选取MPC8641D处理器为硬件测试平台,采用符合VSIPL标准的VSI/Pro Core矢量库和ixlibsav矢量库,通过测试复乘和FFT典型算法不同类型的运算时间,对AltiVec处理单元的矢量运算性能进行了测试评估。通过对测试结果的分析,基于AltiVec技术的PowerPC处理器具备强大的矢量运算处理性能,可以满足嵌入式数字信号处理技术对高性能处理器的需求。     

纯电动汽车磷酸铁锂电池组的建模及优化

鉴于传统神经网络和支持向量机机理复杂、计算量大的缺陷,很难实时跟踪磷酸铁锂电池组复杂快速的内部反应,影响电池荷电状态的估算精度,提出应用一种简单、有效的极限学习机对一额定容量为100Ah、额定电压为72V的纯电动汽车磷酸铁锂电池组建模,并分别与BP神经网络、RBF神经网络、支持向量机进行对比。随后,以学习时间和泛化性能为优化目标,应用粒子群方法寻找最佳隐层节点个数。结果表明,基于极限学习机的磷酸铁锂电池组模型的学习时间、泛化性能优于BP神经网络、RBF神经网络、支持向量机;隐层节点优化后,模型的学习时间和泛化性能达到最优。     

基于PSO-SVR算法的氧乐果合成过程建模研究

针对氧乐果合成过程中温度控制具有参数时变、时滞后、非线性的特点,提出了一种基于改进粒子群算法的支持向量回归的建模方法。对于支持向量回归模型,3个参数(ε,C,γ)的选取很大程度上决定了其拟合的精度和泛化能力的好坏,采用改进的粒子群算法对参数(ε,C,γ)进行同时寻优,建立了改进的氧乐果合成过程PSO-SVR回归模型,该模型具有很好的学习能力和推广能力。实验结果表明,模型较好地体现了系统的动态特性,可用于氧乐果合成过程的模型预估控制,提高系统的控制品质。     

基于功率谱包络能量和SVM的舰用发动机故障诊断

发动机是军舰上的重要部件之一,其稳定性对军舰的正常航行具有重要影响。以舰用发动机关键部件(主泵轴承)为具体研究对象,提出了基于功率谱包络能量和支持向量机相结合的故障诊断方法。首先获取了大量可表征舰用发动机主泵轴承健康状态的振动加速度信息,对其进行功率谱分析,获得其功率谱的包络能量;以获取的舰用发动机主泵轴承功率谱的包络能量构建特征向量,并设计基于SVM的舰用发动机主泵轴承故障诊断模型,对主泵轴承的故障进行诊断研究。研究结果表明,采用基于功率谱包络能量和SVM相结合的舰用发动机关键部件故障诊断方法,可以很好实现主泵轴承的故障诊断效能,为舰用发动机主泵轴承故障诊断的工程应用奠定了基础。     

基于频带分解和距离加权的单矢量水听器浅海被动测距方法研究

本文针对浅海环境下中低频宽带脉冲声源被动测距问题, 提出了一种声压和水平振速联合处理的被动测距方法. 在浅海波导中, 声压和质点振速的自相关函数卷绕(warping)谱具有稳定的频率特征. 声压和水平振速的自相关卷绕谱具有相同的准线谱特征, 垂直振速自相关卷绕谱具有宽谱叠加线谱的特征, 与声压自相关卷绕谱相比, 其尖峰个数比更多, 且尖峰宽度更宽. 利用引导源, 本文提出了基于频带分解和距离加权的声压和水平振速联合被动测距方法. 利用该方法对2008年冬季青岛海域综合实验中单矢量水听器接收的气枪信号进行处理, 结果表明, 该方法能够实现气枪声源的有效测距. 与传统单声压水听器被动测距方法相比, 该方法可以有效减小代价函数的主瓣宽度、提高测距精度.     

大气折射对可见光波段辐射传输特性的影响

折射是影响辐射传输的重要因素. 为分析大气折射对辐射传输的影响, 基于Monte Carlo方法, 给出了考虑大气折射的矢量辐射传输模型, 实现了均匀气层和耦合面处光子随机运动过程的模拟, 实现了直射光及漫射光Stokes矢量、偏振度和辐射通量等参数的计算. 在考虑和不考虑大气折射两种条件下, 验证了模型的准确性; 在纯瑞利散射条件下, 讨论了大气折射对不同方向漫射光Stokes矢量的影响; 在不同太阳天顶角、大气廓线、气溶胶及含云大气条件下, 分析了大气折射对辐射传输过程的影响. 结果表明: 大气折射对漫射光Stokes矢量的影响主要体现在天顶角70°–110°区间, 且随着太阳入射角增大, 其影响更为显著; 不同大气廓线情形下, 大气折射对Stokes矢量的影响不一致, 其原因是不同大气廓线对应的折射率廓线存在差异. 含云及含气溶胶大气条件下, 大气折射对辐射传输的影响变弱, 沙尘型及海盐型气溶胶条件下, 折射对辐射传输的影响强于可溶型气溶胶情形; 不同形状气溶胶条件下, 大气折射对辐射传输的影响也存在显著差异; 不同云高条件下, 大气折射对漫射光Stokes矢量的影响无显著差异, 但随着云光学厚度增大, 大气折射的影响减弱.