• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.135-138
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• 2005

In this paper, we consider the problem of testing for parameter change in time series models based on a cusum of squares. Although the test procedure is well-established for the mean and variance in time series models, a general parameter case was not discussed in literatures. Therefore, here we develop the cusum of squares type test for parameter change in a more general framework. As an example, we consider the change of the parameters in an RCA(1) model. Simulation results are reported for illustration.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.43-48
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• 2005

We consider the problem of testing for parameter changes in time series models based on a cusum test. Although the test procedure is well-established for the mean and variance in time series models, a general parameter case has not been discussed in the literature. Therefore, here we develop a cusum test for parameter change in a more general framework. As an example, we consider the change of the parameters in an RCA(1) model and that of the autocovariances of a linear process. We also consider the variance change test for unstable models with unit roots and GARCH models.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.279-285
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• 2002

In this paper we consider the problem of testing for structural changes in ARIMA models based on a cusum test. In particular, the proposed test procedure is applicable to testing for a change of the status of time series from stationarity to nonstationarity or vice versa. The idea is to transform the time series via differencing to make stationary time series. We propose a graphical method to identify the correct order of differencing.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.748-750
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• 1995

An early detection of cancer is very important for the complete cure of cancer. Therefore, it is considered a diagnosis of cancer via the detection of an abrupt change from the healthy state to the cancerous state. It includes the development of algorithm for the detection of parameter change for conditionally-linear stochastic systems for the cancer diagnosis. The statistical testing is proposed to implement a parameter change algorithm. The detection algorithm studied in this research is based on sequential hypotheses testing in a so-called local asymptotic framework. Here a simple numerical example is provided to highlight some of the concepts and to provide a basis for further investigation. Despite its simplicity this research may have practical application in clinical oncology.

• Journal of the Korean Mathematical Society
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• v.52 no.3
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• pp.503-522
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• 2015

In this paper, we consider the problem of testing for a parameter change in nonlinear time series models with GARCH type errors. We introduce two types of cumulative sum (CUSUM) tests: estimates-based and residual-based tests. It is shown that under regularity conditions, their limiting null distributions are the sup of independent Brownian bridges. A simulation study is conducted for illustration.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.353-359
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• 2003

This paper deals with the problem of testing mean change with one change-point with the normal random variables. We propose a test with the mean difference for change in a location parameter. A power comparison study of various change-point test statistics is performed via Monte Carlo simulation with S-plus software.

• Journal of the Korean Statistical Society
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• v.25 no.4
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• pp.577-588
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• 1996

The Bayes factor provides a possible hierarchical Bayesian approach for studying the change point problems. A hypothesis for testing change versus no change is considered using predictive distributions. When the underlying distribution is in one-parameter exponential family with conjugate priors, Bayes factors are investigated to the hypothesis above. Finally one example is provided .

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.107-117
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• 2004

Change point testing problem is considered. Kernel density estimators are used for constructing proposed change point test statistics. The proposed method can be used to the hypothesis testing of not only parameter change but also distributional change. Bootstrap method is applied to get the sampling distribution of proposed test statistic. Small sample Monte Carlo Simulation were also conducted in order to show the performance of proposed method.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.185-189
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• 2001

In this paper, we consider the problem of testing for parameter changes in time series models based on a sequential test. Although the test procedure is well-established for the mean and variance change, a general parameter case has not been discussed in the literature. Therefore, we develop a sequential test for parameter changes in a more general framework.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1551-1559
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• 2013

In this paper, we analyze the Korean Won/Japanese 100 Yen exchange rate data based on the ARMA-GARCH model, and perform the test for detecting the parameter changes. As a test statistics, we employ the cumulative sum (CUSUM) test for ARMA-GARCH model, which is introduced by Lee and Song (2008). Our empirical analysis indicates that the KRW/JPY exchange rate series experienced several parameter changes during the period from January 2000 to December 2012, which leads to a fitting of AR-IGARCH model to the whole series.