基于K-S检验的自由分布过程变点识别及其应用

以不需要过程分布假设前提的自由分布变点识别问题为研究对象,针对单观测数据序列提出基于K-S检验的变点识别流程,以上证A股日收盘价序列数据为依据验证识别方法的有效性,仿真性能测试结果表明新方法适合多种不同类型的分布过程,其综合性能优于其他类型方法.     

重尾序列均值变点的经验似然比检验

本文通过经验似然思想建立假设检验的方法,研究了重尾序列均值变点的检测问题.首先,基于重尾模型,在原假设和备择假设下得到经验似然函数.其次,基于经验似然函数构造似然比检验统计量,并给出在原假设成立时该似然比统计量的渐近分布.最后,进行Monte Carlo数值模拟验证该方法的有效性,模拟结果表明本方法对重尾序列均值变点的...     

基于非参数方法的交通流变点问题分析

考虑到交通流数据的分布形式不确定及其变点数目未知的实际情形,以基于非参数方法的交通流变点问题为研究对象,拟通过基于Kolmogorov-Smirnov(KS)检验和Mann-Whitney U检验的滑动窗口法实现变点存在与否的检验,进一步结合二分法对交通流数据变点数目及其位置进行估计.正态分布模拟仿真显示,两种方法对于均值变点检验和估计效果较好,而对于方差变点检验和估计,Mann-Whitney U方法不及K-S方法.最后,贵阳市中心道路车流量数据实例分析,表明方法对于交通流突变分析效果较好,可为相关部门提供可靠的决策依据.     

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.     

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.     

自正则化大偏差的一个注记

设$X_1,X_2,\cdots$为一列独立同分布的随机变量序列\bd 邵(1997)在没有任何矩条件下建立了自正则化大偏差定理, 但其上界的证明相当复杂\bd 为此, 本文给出了一个简洁的证明     

随机排列方法检验方差变点

给出了随机排列的主要性质及证明。构造了随机排列检验方差变点的统计量。以GARCH(1,1)过程为例,模拟比较了随机排列方法与近似极限分布方法关于方差变点检验的临界值。应用随机排列方法检测人民币兑美元汇率的变点,并与惩罚对比函数方法作比较。模拟与实证结果均表明随机排列方法检验方差变点是灵活有效的。     

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。     

Percentage points and power of a K-S type test for linearity in autoregressive time series

1. IntroductionA Kolmogorov-Smirnov type test for linearity in autoregressive models has been con-sidered by An and Chengl']. They showed that the asymptotic null di8tribution of the teststati8tic is related to the supremum of the 8tandard Brownian motion. Their simulationshowed that the test was easy to conduct and more powerful than the tests of Hinich[2],Keenanl3j, Tasyl4] and Chan and TOngl5]. However, the percentage points of the null dis-tribution of the test have not been tabulated…     

Self-normalization: Taming a wild population in a heavy-tailed world

The past two decades have witnessed the active development of a rich probability theory of Studentized statistics or self-normalized processes, typified by Student's t-statistic as introduced by W. S. Gosset more than a century ago, and their applications to statistical prob-lems in high dimensions, including feature selection and ranking, large-scale multiple testing and sparse, high dimensional signal detection. Many of these applications rely on the robust-ness property of Studentization/self-normalization against heavy-tailed sampling distributions. This paper gives an overview of the salient progress of self-normalized limit theory, from Studen-t's t-statistic to more general Studentized nonlinear statistics. Prototypical examples include Studentized one- and two-sample U-statistics. Furthermore, we go beyond independence and glimpse some very recent advances in self-normalized moderate deviations under dependence.     

带趋势项的ARCH误差模型参数变化的残量检验

This paper analyzes the problem of testing for parameters change in ARCH errors models with deterministic trend based on residual cusum test. It is shown that the asymptotically limiting distribution of the residual cusum test statistic is still the sup of a standard Brownian bridge under null hypothesis. In order to check this, we carry out a Monte Carlo simulation and examine the return of IBM data. The results from both simulation and real data analysis support our claim. We also can explain this phenomenon from a theoretical viewpoint that the variance in ARCH model in mainly determined by its parameters.     

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.     

基于统计变点检测的国际天然铀价格影响因素分析

铀资源是军民两用的重要战略资源,有必要研究其价格变化为政府决策提供依据.采用基于Schwarz信息准则的统计变点检测方法,识别出1990-2013年国际天然铀价格的多个均值-方差变点,据此将国际天然铀价格的变化分为稳中下降期、大幅上扬期、震荡回归期和持续下降期四个阶段.研究结果表明,核事故、二次铀源、核电政策是影响国际天然铀价格的重要因素,同时天然铀逐渐显示出其商品的属性,其价格受供需关系的影响明显.     

A New Method for Testing Leverage Effect

Leverage effect often arises in many fields,such as financial risk management, portfolio and option pricing. However,it still remains to be studied that whether there is leverage effect or not in real data. Based on local polynomial regression estimation and Kolmogorov-Smirnov nonparametric test, this paper introduces a new nonparametric test statistic for the leverage effect, and some asymptotic properties are also presented. Simulation studies show that the proposed method performs well. Finally, empirical studies on SP500 index and Microsoft data imply that leverage effect exists in the real data, which is consistent with the idea in finance.     

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??Leverage effect often arises in many fields,such as financial risk management, portfolio and option pricing. However,it still remains to be studied that whether there is leverage effect or not in real data. Based on local polynomial regression estimation and Kolmogorov-Smirnov nonparametric test, this paper introduces a new nonparametric test statistic for the leverage effect, and some asymptotic properties are also presented. Simulation studies show that the proposed method performs well. Finally, empirical studies on SP500 index and Microsoft data imply that leverage effect exists in the real data, which is consistent with the idea in finance.     

随机设计下非参数回归模型变点的小波检测与估计

A wavelet method of detection and estimation of change points in nonparametric regression models under random design is proposed. The confidence bound of our test is derived by using the test statistics based on empirical wavelet coefficients as obtained by wavelet transformation of the data which is observed with noise. Moreover, the consistence of the test is proved while the rate of convergence is given. The method turns out to be effective after being tested on simulated examples and applied to IBM stock market data.