Test for Parameter Change in Diffusion Processes by Cusum Statistics Based on One-step Estimators

In this paper, we consider the problem of testing for a parameter change using the cusum test based on one-step estimators in diffusion processes. It is shown that under regularity conditions the cusum test statistic has the limiting distribution of a functional of Brownian bridge.     

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.     

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.

     

Test for parameter change based on the estimator minimizing density-based divergence measures

In this paper we consider the problem of testing for a parameter change based on the cusum test proposed by Leeet al. (2003,Scandinavian Journal of Statistics,30, 781–796). The cusum test statistic is constructed via employing the estimator minimizing density-based divergence measures. It is shown that under regularity conditions, the test statistic has the limiting distribution of the sup of standard Brownian bridge. Simulation results demonstrate that the cusum test is robust when outliers exist.     

Test for parameter change in stochastic processes based on conditional least-squares estimator

In this paper, we consider the problem of testing for a parameter change in stochastic processes. In performing a test, we employ the cusum test considered in Lee et al. (Scand. J. Statist. 30 (2003) 651). The cusum test is based on the conditional least-squares estimator introduced by Klimko and Nelson (Ann. Statist. 6 (1978) 629). Special attention is paid to the nonlinear autoregressive processes including TAR and ARCH processes. It is shown that under regularity conditions, the test statistic behaves asymptotically the same as the sup of the squares of independent standard Brownian bridges. Simulation results as to ARCH(1) processes and an example of real data analysis are provided for illustration.     

A two-dimensional logistic model for the interaction of demand and supply and its bifurcations

A two-dimensional logistic model is used to describe the interactions and evolution of potential demand and supply. It is assumed that the demand policies will change the growth capacity of the demand and a parameter is introduced to describe its effect. The qualitative analysis shows that the parameter will affect the dynamical behavior of the system. Bifurcations of the system under different conditions are obtained. Under certain conditions, a small shift of policies may cause a sudden change of the demand.     

Moving estimates test with time varying bandwidth

In this paper, we consider the problem of testing for parameter changes in time series models based on a moving estimates (ME) test. It is widely accepted that detecting some changes, for instance, those caused by temporary parameter shifts by the existing cusum test is difficult. A MV test with a fixed bandwidth has been developed to circumvent the defect, but the test still does not perform well under certain conditions. Motivated by this, we propose a MV test with a time varying bandwidth to outperform the original test. In order to illustrate our findings, we have provided simulation results.     

The performance of a dynamic stability test for moisture sensitivity

The quality of parameter estimates evaluated by the authors from dynamic stability tests which identify kinetic models in the presence of a continually changing moisture environment, is evaluated with respect to their static test counterparts using computer simulated tests. For both the static and dynamic tests, the results show that to gain statistical confidence in the parameter estimates, the relative experimental noise must be minimized (minimize the measurement error and maximize the extent of degradation). The dynamic test procedure yields parameter estimates with less confidence than the static test, because the total extent of degradation encountered within the test is less, hence the relative noise is higher. Therefore, the dynamic stability test is inferior to its static counterpart when all of the assumptions about the system are met, but since the dynamic test evaluates the model parameters under conditions similar to the real system, factors unconsidered in the model are minimized.     

On the Cramér–von Mises test with parametric hypothesis for poisson processes

The problem of the goodness of-fit testing for inhomogeneous Poisson process with parametric basic hypothesis is considered. A test statistic of the Cramér–von Mises type with parameter replaced by the maximum likelihood estimator is proposed and its asymptotic behavior is studied. It is shown that in the case of shift parameter, the limit distribution of the test statistics (under hypothesis) does not depend on the true value of this parameter.     

Comparison of two variances under inequality constraints by using empirical likelihood method

In this article, the empirical likelihood introduced by Owen Biometrika, 75, 237-249 （1988） is applied to test the variances of two populations under inequality constraints on the parameter space. One reason that we do the research is because many literatures in this area are limited to testing the mean of one population or means of more than one populations; the other but much more important reason is： even if two or more populations are considered, the parameter space is always without constraint. In reality, parameter space with some kind of constraints can be met everywhere. Nuisance parameter is unavoidable in this case and makes the estimators unstable. Therefore the analysis on it becomes rather complicated. We focus our work on the relatively complicated testing issue over two variances under inequality constraints, leaving the issue over two means to be its simple ratiocination. We prove that the limiting distribution of the empirical likelihood ratio test statistic is either a single chi-square distribution or the mixture of two equally weighted chi-square distributions.     

Test for parameter change in discretely observed diffusion processes

In this paper, we consider the problem of testing for the parameter change in discretely observed diffusion processes. For a test, we perform the cusum test based on the estimator proposed by Kessler (J Stat 24:211–229, 1997). It is shown that the test statistic weakly converges to the sup of the square of a Browian bridge. Simulation results are illustrated for the Ornstein-Uhlenbeck and Cox-Ingersoll-Ross processes.     

On asymptotically distribution free tests with parametric hypothesis for ergodic diffusion processes

We consider the problem of the construction of the asymptotically distribution free test by the observations of ergodic diffusion process. It is supposed that under the basic hypothesis the trend coefficient depends on a finite-dimensional parameter and we study the Cramér-von Mises type statistics. The underlying statistics depends on the deviation of the local time estimator from the invariant density with parameter replaced by the maximum likelihood estimator. We propose a linear transformation which yields the convergence of the test statistics to an integral of the Wiener process. Therefore the test based on this statistics is asymptotically distribution free.     

Testing the Gumbel Hypothesis Via the Pot-Method

We consider an i.i.d. sample, generated by some distribution function, which belongs to the domain of attraction of an extreme value distribution with unknown shape and scale parameters. We treat the scale parameter as a nuisance parameter and establish for the hypothesis of Gumbel domain of attraction an asymptotically optimal test based on those observations among the sample, which exceed a given threshold sequence. Asymptotic optimality is achieved along certain contiguous extreme value alternatives within the concept of local asymptotic normality (LAN). Adaptive test procedures exist under restrictive assumptions. The finite sample size behavior of the proposed test is studied by simulations and it is compared to that of a test based on the sample coefficient of variation.     

Sequential monitoring of a Bernoulli sequence when the pre-change parameter is unknown

The task of monitoring for a change in the mean of a sequence of Bernoulli random variables has been widely studied. However most existing approaches make at least one of the following assumptions, which may be violated in many real-world situations: (1) the pre-change value of the Bernoulli parameter is known in advance, (2) computational efficiency is not paramount, and (3) enough observations occur between change points to allow asymptotic approximations to be used. We develop a novel change detection method based on Fisher’s exact test which does not make any of these assumptions. We show that our method can be implemented in a computationally efficient manner, and is hence suited to sequential monitoring where new observations are constantly being received over time. We assess our method’s performance empirically via using simulated data, and find that it is comparable to the optimal CUSUM scheme which assumes both pre- and post-change values of the parameter to be known.     

Orbital stability of floating periodic peakons for the Camassa–Holm equation

Considered herein is the orbital stability of floating periodic peakons for the Camassa–Holm (CH) equation, which describes one-dimensional surface waves at a free surface of shallow water under the influence of gravity. The floating periodic peakons shift up or down according to the change of the parameter. The result shows that the floating periodic peakons are orbitally stable and their stability is independent of the parameter in the CH equation.     

油价结构性变化检验与动态监控研究

本文运用参数稳定性检验方法对油价动态波动路径进行研究,发现油价序列存在显著的结构性改变特征,检测出自1974年2月以来油价共经历了四次结构性改变过程,本文考察了导致结构性改变的根本原因以及与重大事件的关系,并给出了结构改变点的点估计和区间估计,同时根据结构性改变情况将油价价位波动过程划分为五个阶段。此外,本文还讨论了结构性改变的监控问题,在历史数据的基础上及时发现新的结构改变点,为油价走势预测和和石油市场形势判断提供参考依据。     

正压大气模式下大地形和β变化的Rossby波

在正压大气模式下从准地转位涡方程出发,考虑地形和β随纬度变化下引进参数δ对Rossby波的共同作用,应用正交模方法得到在中高纬度具有大地形、Froude数以及参数δ的Rossby波相速度公式; 分析β变化下大地形和Froude数对Rossby波稳定度的影响,表明大地形、Froude数和参数δ对Rossby波的稳定性作用.     

Subsampling tests for variance changes in the presence of autoregressive parameter shifts

In this paper, we consider the problem of testing for variance changes in the linear autoregressive processes including AR(p) processes when there are autoregressive parameter shifts. In performing a test, we employ the conventional residual CUSUM of squares test (RCUSQ) statistic. The RCUSQ test is based on the subsampling method introduced by Jach and Kokoszka (2004) [16] to eliminate the influence caused by autoregressive parameter shifts. It is shown that under regularity conditions, the test statistic behaves asymptotically the function of a standard Brownian bridge. We establish the asymptotic validity of this method and assess its performance both theoretically and numerically.     

APPROXIMATE POWER OF HETEROSCEDASTICITY TEST IN NONLINEAR MODELS WITH ARIMA（0,1,0） ERRORS

This paper presents an approach for estimating power of the score test, based on an asymptotic approximation to the power of the score test under contiguous alternatives. The method is applied to the problem of power calculations for the score test of heteroscedasticity in European rabbit data （Ratkowsky, 1983）. Simulation studies are presented which indicate that the asymptotic approximation to the finite-sample situation is good over a wide range of parameter configurations.     

ON TESTING THE EQUALITY OF K MULTIPLE AND PARTIAL CORRELATION COEFFICIENTS

1 IntroductionWe are given k independent Wishart densities of the p x p random symmetric positivedefinite matrices G1, G2,' t Gkwhere Ri denotes the population correlation matrir, and K as a generic letter denotes thenormalizing constants of density functions in this paper. The squared multiple correlationcoefficient of the i-th population is pf = r:Riz1.)ri, whereand a likelihood ratio test for testing the hypothesisis desired.Now Wilks (l932, l946) provides certain procedures and guidel…