空间滞后模型中Moran's I统计量的Bootstrap检验

针对空间滞后模型的估计残差,采用Wild.Bootstrap方法进行空间相关性检验;进而,基于Moran's I统计量的经验分布,从水平扭曲和功效角度比较Bootstrap检验和渐近检验的有效性.Monte Carlo实验结果显示,在经典正态假设条件下,Bootstrap检验已然同等或优于渐近检验;在更为实际的异方差、非正态假设条件下,渐近检验显著偏离,而Bootstrap检验的水平扭曲更小、功效更高.当模型不满足经典的分布假设条件,尤其是在小样本和空间衔接密度较高情况下,与渐近检验相比,Bootstrap检验更为有效.     

空间滞后模型中Moran's Ⅰ统计量的Bootstrap检验

针对空间滞后模型的估计残差,采用Wild Bootstrap方法进行空间相关性检验;进而,基于Moran's Ⅰ统计量的经验分布,从水平扭曲和功效角度比较Bootstrap检验和渐近检验的有效性.Monte Carlo实验结果显示,在经典正态假设条件下,Bootstrap检验已然同等或优于渐近检验;在更为实际的异方差、非正态假设条件下,渐近检验显著偏离,而Bootstrap检验的水平扭曲更小、功效更高.当模型不满足经典的分布假设条件,尤其是在小样本和空间衔接密度较高情况下,与渐近检验相比,Bootstrap检验更为有效.     

主动学习可靠性分析算法:基于Kriging预测方差的视角

针对基于Kriging模型的主动学习可靠性方法 (active learning Kriging-Monte Carlo simulation, AK-MCS)中, Kriging预测方差低估导致评估过程存在选点和停止判断失误的问题,提出一种基于Bootstrap Kriging的主动学习可靠性方法 (Bootstrapped active learning Kriging-Monte Carlo simulation, BAK-MCS).首先拟合真实功能函数初始Kriging模型,然后使用Bootstrap Kriging方差构造BU学习函数,从而进行主动序贯采样更新Kriging模型,最后采用收敛Kriging模型结合蒙特卡罗模拟(Monte Carlo simulation, MCS)估计结构失效概率.数值算例结果表明,与MCS和AK-MCS方法相比, BAK-MCS在保持高预测精度的同时减少了真实功能函数的调用次数,提高了可靠性评估建模效率.     

周期异方差时间序列中季节单位根的Gauss 检验

为检验带异方差的季节时间序列中的单位根,提出了基于Cauchy估计的Zc统计量.在原假设下得到该检验统计量的极限分布服从于标准正态分布,并与季节周期d和误差项的周期异方差无关.Monte Carlo模拟计算表明当模型中自回归系数接近1时,用该方法得到的估计值比普通的最小二乘方法得到的估计值误差小.计算结果和实例分析结果表明了用该方法检验季节单位根的简便性和有效性.     

有杆泵抽油系统可靠性数字仿真研究

利用可修系统可靠性分析理论，提出在役有杆泵抽油系统工作可靠性数字仿真模型，在对现场数据收集，整理的基础上，推断各子系统寿命和维修时间的统计模型，运用Monte Carlo法通过抽样模拟对系统可靠性和维修性进行数字仿真，实现对系统可靠性的定量评价。     

基于小波的非参数回归模型均值变点的Bootstrap监测

本文首次考虑非参数回归模型均值函数结构变点的在线监测问题. 首先对回归函数的局部线性估计值进行小波变换， 基于得到的小波系数构造监测统计量， 并在原假设和备择假设下推导出监测统计量的渐近分布； 为提高监测效果， 进一步构造了Bootstrap在线监测方法， 定义了停时； 模拟结果和实例分析显示该方法可以很好地监测到变点， 并具有较短的检测延迟.     

春节模型的效应期识别方法研究

春节模型的参数设定目前更多依赖于研究者的主观判断.为解决这一问题,本文首先介绍了春节因素调整的一般过程以及模型设定和检验的相关细节.在此基础上,提出一种基于"循环遍历"方式和序贯检验方法来自动选择春节模型的最优参数组合的新思路.并以社会消费品零售总额月度序列的春节模型的最优效应期长度识别为例,检验该方法的可行性和有效性.实证结果显示,基于季节峰值、离群值点、Q统计量、AICC值和BIC值等统计量的序贯检验能够有效识别出春节模型的最优效应期长度,进而改善春节模型的季节调整性能.     

基于固定滞后滤波平滑的弹道重构算法

为有效利用雷达测量的飞行数据进行弹箭的实际弹道重构,建立了弹道重构需要的状态方程和量测方程,考虑模型的非线性和重构状态非高斯分布的可能性,结合Bootstrap粒子滤波给出一种一步固定滞后滤波平滑算法的实现,并将其作为弹道重构的估计工具。算例仿真表明,相比于Bootstrap粒子滤波和无迹卡尔曼滤波,该Monte Carlo平滑算法可以进一步提高估计的精度,为弹道重构提供一种新的有效工具。     

导弹子母弹抛撒的均匀性指标的

本文以导弹侵彻子母弹抛撒为背景，提出了一种适合于有限子弹抛撒的均匀性指标，并且运 用现代统计方法——Bayes Bootstrap法模拟了均匀性指标统计量的分布；另外，从火力 运用的观点出发，根据子母弹抛撒的均匀性指标的大小对战斗部毁伤效率的影响，确定了 均匀性指标的容许下限；应用统计检验方法给出了子母弹抛撒的均匀性的评定方法，为导弹侵彻子母弹抛撒的鉴定及同类武器该项目的鉴定提供可信且便于操作的方法。     

具有未知跳跃股票价格异方差性的 Bootstrap模拟检验

采用Bootstrap模拟方法检验股票价格异方差性．利用随机分析方法获得波动率的一个渐进估计，这种估计能够消除股票价格中可能存在的随机跳跃的影响，并且无需确定波动率函数的具体形式、建立了一个关于波动率为常数的原假设的统计量，用Bootstrap模拟方法可以获得该统计量的分布．用此检验方法对中国沪深两市指数进行检验，获得了两市中指数呈异方差性的结论．这种检验方法对认识市场微观结构、资产定价、投资和风险管理都具有积极作用．     

Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects

This paper applies bootstrap methods to LM tests(including LM-lag test and LM-error test) for spatial dependence in panel data models with fixed effects, and removes fixed effects based on orthogonal transformation method proposed by Lee and Yu(2010). The consistencies of LM tests and their bootstrap versions are proved, and then some asymptotic refinements of bootstrap LM tests are obtained. It shows that the convergence rate of bootstrap LM tests is O((N T)~(-2)) and that of fast double bootstrap LM tests is O((NT)~(-5/2)). Extensive Monte Carlo experiments suggest that,compared to aysmptotic LM tests, the size of bootstrap LM tests gets closer to the nominal level of signifiance, and the power of bootstrap LM tests is higher, especially in the cases with small spatial correlation. Moreover, when the error is not normal or with heteroskedastic, asymptotic LM tests suffer from severe size distortion, but the size of bootstrap LM tests is close to the nominal significance level.Bootstrap LM tests are superior to aysmptotic LM tests in terms of size and power.     

Regression Function Comparison for Paired Data

In this paper, the regression function comparison for paired data is studied. The proposed test statistic is based on the weighted integral of characteristic function marked by the difference of responses. There are several merits of the proposed statistic. For instance, it takes a simple V-statistic form. No bandwidth is needed. No moment conditions are required for covariates. It can be applied to covariates of any fixed dimension. The asymptotic results are also developed. It is proven that n times the proposed test statistic converges to a finite limit under the null hypothesis and the test is consistent against any fixed alternatives. Local alternative hypotheses which converge to the null hypothesis at the rate of n-1/2 are also detected. A suitable Bootstrap algorithm is also proposed for the implementation of the proposed test statistic. Simulation studies are carried out to illustrate the merits of the proposed method. A real data example is also used to illustrate the proposed testing procedures.     

单位根检验中的Wald检验量研究：Bootstrap法VS临界值法

针对Wald临界值检验方法存在检验水平扭曲和功效低下的不足,提出了Wald检验量的Bootstrap检验方法,并从理论上证明了该方法的有效性;借助于蒙特卡罗模拟技术对两种检验方法进行对比.结果表明：在无漂移项数据生成过程中,两种方法的实际覆盖率分别为100%和72.9%,精确程度之比为33：15;在有漂移项的数据生成过程中,实际覆盖率分别为94.4%和77.8%,精确程度之比为20：15;在检验功效方面,Bootstrap方法较临界值检验具有明显的优势,尤其是在小样本下;由标准正态分布得到的临界值对其它误差分布类型缺乏稳健性.实证分析也表明由Bootstrap检验方法得到的模型更符合实际情况.因此,Bootstrap方法可以替代临界值方法进行单位根的Wald检验.     

基于仿真的复杂系统可靠性评估输入数据统计分析研究

对应用仿真技术评估复杂系统可靠性时榆入数据的统计分析进行了深入研究。特别是对试验数据较少，即小样本情况进行了研究。对于根据小样本进行的可靠性估计，结合Bayes方法的蒙特卡洛(Bayes-MC)方法和结合改进的Bootstrap方法的蒙特卡洛(改进的Bootstrap-MC)方法是比较有效的。概括总结了无数据情况下的专家经验估计三角分布方法，提出了改进的Bootstrap方法，将验前信息与专家经验纳入Bootstrap方法中，克服了该方法利用样本信息量不足的缺陷，使其更加完善与实用。在复杂系统的可靠性评估时，应采用综合或混合的方法。     

Statistical Diagnosis for HIV Dynamics Based on Mean Shift Outlier Model

Ordinary differential equation(ODE) are widely used for quantifying HIV viral dynamics.It is interesting but challenging to estimate ODE parameters from noisy data, especially when the data have some outliers. In this study, the authors use the Mean Shift Outlier Model(MSOM) to detect outliers in HIV model based on the two-step estimation of ODE. Approximate formula for shift parameter is derived. Furthermore, a score test statistic is constructed and its approximating distribution is established. The simulation results show that: 1) The boundary points have more impact on the parameter estimation relative to interior points. 2) The proposed procedure can detect the outliers effectively. The authors illustrate the proposed approach using an application example from an HIV clinical trial and find similar pattern to the simulation studies.     

Distance-Based Regression Analysis for Measuring Associations

Distance-based regression model, as a nonparametric multivariate method, has been widely used to detect the association between variations in a distance or dissimilarity matrix for outcomes and predictor variables of interest in genetic association studies, genomic analyses, and many other research areas. Based on it, a pseudo-F statistic which partitions the variation in distance matrices is often constructed to achieve the aim. To the best of our knowledge, the statistical properties of the pseudo-F statistic has not yet been well established in the literature. To fill this gap, the authors study the asymptotic null distribution of the pseudo-F statistic and show that it is asymptotically equivalent to a mixture of chi-squared random variables. Given that the pseudo-F test statistic has unsatisfactory power when the correlations of the response variables are large, the authors propose a square-root F-type test statistic which replaces the similarity matrix with its square root. The asymptotic null distribution of the new test statistic and power of both tests are also investigated. Simulation studies are conducted to validate the asymptotic distributions of the tests and demonstrate that the proposed test has more robust power than the pseudo-F test. Both test statistics are exemplified with a gene expression dataset for a prostate cancer pathway.

     

相似产品步进应力加速寿命试验的统计分析

本文将单组样本的步进应力加速寿命试验(SSALT)拓展到双样本情形.基于累积失效模型(CEM),对两组指数产品的联合Ⅱ型截尾数据进行综合分析.首先,计算未知参数的极大似然估计(MLE),并利用矩母函数(MGF)建立其条件概率分布.然后,采用两种方法,基于概率分布的精确分析方法和参数的Bootstrap方法,构建参数的置信区间(CIs).以覆盖率作为评价标准,对两种方法的区间估计效果进行分析.最后,通过数值算例验证了本文方法的有效性.     

Empirical likelihood for partially linear models under negatively associated errors

This paper proposes to use the blockwise empirical likelihood(EL) method to construct the confidence regions for the regression vector β in a partially linear model under negatively associated errors. It is shown that the blockwise EL ratio statistic for β is asymptotically χ~2 distributed. The result is used to obtain an EL-based confidence region for β. Results of a simulation study on the finite sample performance of the proposed confidence regions are reported.