位置参数变点的非参数检验及其渐近性质

本文基于U-统计量,对于位置参数模型,讨论了位置参数变点的检验问题,给出了检验统计量并研究它的分布的极限性质,证明了检验统计量的极限分布是sup |B(t)|,其中{B(t),0＜t＜1}是一个Brown桥.将此结果应用到了双参数指数分布和Weibu11分布尺度参数变点的检验问题中.     

尺度参数变点的非参数检验

本文讨论了尺度参数模型参数变点的假设检验问题\bd 基于两样本$U$\,-统计量, 我们给出了两个检验, 并且研究了检验统计量分布的极限性质\bd 我们证明了这两个检验统计量的极限分布分别是$\sup\limits_{0相似文献   

广义Pareto分布变点检测似然比模型

为检测极端事件的状态变化,基于似然比方法研究了广义Pareto分布(Generalized Pareto Distribution,GPD)变点检测模型.考虑三参数GPD变点的检验问题,提出了最大似然比检验统计量.通过证明参数变换后GPD的对数似然和检验统计量的一系列极限性质,得到了检验统计量的渐近分布.通过模拟研究,对该方法的有限样本性质进行了评价,实例分析也验证了该方法的可行性.     

非参数模型均值函数结构变点的Bootstrap检测

本文检测非参数回归模型均值函数结构变点,针对均值函数跃度的长期均值为零时,基于残量的CUSUM统计量对均值函数结构变点检验无效的问题,本文提出了一种基于均值函数的核估计的检验统计量,得到统计量在原假设和备择假设下的极限分布,并构造Bootstrap方法对非参数回归模型均值函数结构变点进行检验,证明了检验和估计的一致性;模拟结果表明本文方法明显优于已有方法。     

测量误差模型只有一个变点的检验和估计

本文讨论了测量误差模型中参数只有一个变点的检验和估计问题，首先，给出其似然比检验统计量，然后，基于最小信息准则的原理，利用Schwarz信息准则（SIC），在多余参数已知和未知的情况下，分别给出了检验统计量，讨论了利用SIC方法给出的检验统计量的渐近分布，证明了基于似然比方法和SIC方法给出的变点估计是相同的，并且在一定条件下，给出了变点估计的极限分布，运用Monte-Carlo随机模拟的方法，分别给出了以上检验的临界值。     

长相依序列均值变点检测

研究了长相依序列均值变点检测问题.首先给出变点检验的Ratio统计量其次分别推导了原假设和备择假设下统计量的极限分布,最后用蒙特卡洛方法模拟出检验的临界值,并通过数值模拟和实例分析说明方法的有效性.     

基于经验欧氏似然的均值单变点检测

均值单变点检测是研究变点问题的基础.论文根据均值单变点模型的特点,构造截断经验欧氏似然比检验函数并给出显式表达.在此基础上,得到了零假设下检验统计量的极限分布为极值分布,给出变点的诊断方法.在有变点的情况下,进一步给出变点位置的估计和其相合性的理论证明.最后通过数值模拟和尼罗河年流量的实证分析说明所提方法的有效性和实用...     

正态分布方差变点的检验

本文讨论了正态分布方差只有一个变点的检验问题，我们构造了三个检验统计量，其中L检验基于非参数U统计量，B检验基于Bayes方法，R检验由极大似然比方法导出．本文给出了L、B、R检验的渐近临界值，并用MonteCarlo模拟方法研究了这三个检验与平方的CUSUM检验以及LM检验的势，并进行了比较。当变点在序列的前一半位置时，L和R检验较好，当变点在序列的后一半位置时，平方的CUSUM和B检验较好．     

随机设计下非参数回归模型方差变点Ratio检验

研究随机设计下非参数回归模型方差变点Ratio检验.首先用局部多项式方法估计回归曲线得到残差序列,其次基于残差的平方序列构造Ratio检验统计量并推导检验统计量的极限分布.最后数值模拟与实例分析结果表明方法的有效性.     

GARCH模型变点的Ratio检验

研究GARCH模型参数变点的Ratio检验.首先构造了基于残量累积平方和的Ratio统计量,推导了原假设下统计量的极限分布,其次采用Monte Carlo方法检验其有效性,最后以数据为例进一步说明该方法的实用性.     

至多一个分布变点的非参数检验及其渐近性质

本文研究了连续分布函数变点的假设检验问题,通过秩统计量和次序统计量方法,得到了相应的检验统计量及其渐近性质.     

Testing for the change of the mean-reverting parameter of an autoregressive model with stationary Gaussian noise

The likelihood ratio test for a change in the mean-reverting parameter of a first order autoregressive model with stationary Gaussian noise is considered. The test statistic converges in distribution to the Gumbel extreme value distribution under the null hypothesis of no change-point for a large class of covariance structures including long-memory processes as the fractional Gaussian noise.

     

ARCH模型的多变点检验

研究自回归条件异方差(ARCH)模型的多变点检验问题.提出一种拟似然比检验统计量,并在原假设下给出统计量的极限分布.在假设检验过程中得到变点个数的一致估计.数值模拟与实例分析说明了方法的合理性.     

On Mean Change-Point Detection Based on Empirical Euclidean Likelihood

Mean chage-point detection is the groundwork in statistics. The trimmed empirical Euclidean likelihood ratio function is constructed based on the features of change-point in mean model. And the explicit expression is derived. The null limit distribution of the test statistic is investigated with extreme value distribution. And the change-point detection is made. if the change-point exists, it's location and consistency are discussed. Simulations and real analysis of Nile River data show that our proposed method is practicable and effective.     

DISPERSION COMPARISONS OF TWO PROBABILITY VECTORS UNDER MULTINOMIAL SAMPLING

We consider testing hypotheses concerning comparing dispersions between two parameter vectors of multinomial distributions in both one-sample and two-sample cases. The comparison criterion is the concept of Schur majorization. A new dispersion index is proposed for testing the hypotheses. The corresponding test for the one-sample problem is an exact test. For the two-sample problem, the bootstrap is used to approximate the null distribution of the test statistic and the p-value. We prove that the bootstrap test is asymptotically correct and consistent. Simulation studies for the bootstrap test are reported and a real life example is presented.     

Estimation and testing in generalized mean-reverting processes with change-point

In this paper, we study an inference problem in generalized Ornstein–Uhlenbeck processes with an unknown change-point when the drift parameter is suspected to satisfy a linear restriction. The testing problem studied generalizes a very recent problem about testing the existence of a change-point. To this end, we derive the asymptotic properties of the unrestricted estimator (UE) and the restricted estimator for the drift parameters, and we construct some shrinkage estimators (SEs). Further, we derive a test for testing the uncertain restriction and establish its asymptotic power. Moreover, we derive the asymptotic distributional risk of the proposed estimators and we prove that SEs dominate the UE. Finally, we present some numerical results which confirm the consistency of the proposed test as well as the superiority of the SEs over UE.     

艾拉姆咖分布参数变点的Bayes估计

研究了艾拉姆咖分布变点估计的非迭代抽样算法(IBF)和MCMC算法.在贝叶斯框架下,选取无信息先验分布,得到关于变点位置的后验分布和各参数的满条件分布,并且详细介绍了IBF算法和MCMC方法的实施步骤.最后进行随机模拟试验,结果表明两种算法都能够有效的估计变点位置,并且IBF算法的计算速度优于MCMC方法.     

An Analytic Expression for the Distribution of the Generalized Shiryaev–Roberts Diffusion

We consider the quickest change-point detection problem where the aim is to detect the onset of a pre-specified drift in “live”-monitored standard Brownian motion; the change-point is assumed unknown (nonrandom). The topic of interest is the distribution of the Generalized Shryaev–Roberts (GSR) detection statistic set up to “sense” the presence of the drift. Specifically, we derive a closed-form formula for the transition probability density function (pdf) of the time-homogeneous Markov diffusion process generated by the GSR statistic when the Brownian motion under surveillance is “drift-free”, i.e., in the pre-change regime; the GSR statistic’s (deterministic) nonnegative headstart is assumed arbitrarily given. The transition pdf formula is found analytically, through direct solution of the respective Kolmogorov forward equation via the Fourier spectral method to achieve separation of the spacial and temporal variables. The obtained result generalizes the well-known formula for the (pre-change) stationary distribution of the GSR statistic: the latter’s stationary distribution is the temporal limit of the distribution sought in this work. To conclude, we exploit the obtained formula numerically and briefly study the pre-change behavior of the GSR statistic versus three factors: (a) drift-shift magnitude, (b) time, and (c) the GSR statistic’s headstart.     

GARCH模型参数变化的残量检验

本文研究GARCH模型参数变化的检验问题. 给出残量累积和统计量, 在原假设下得到了统计量的极限分布; 模拟结果表明残量检验可以弥补Kim, Cho和Lee (2000)\ucite{1}提出的平方累积和检验的某些不足, 比如经验势函数值过低的问题.