• 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 Environmental Science International
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• v.7 no.1
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• pp.1-7
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• 1998

This paper alms to estimate the change point of the precipitation in Pusan area using the several statistical approaches. The data concerning rainfall are extracted from the annual climatological report and monthly weather report issued by the Korean Meteorological Administration. The average annual precipitation at Pusan is 1471.6 mm, with a standard deviation of 406.0 mm, less than the normal(1486.0 mm). The trend of the annual precipitation is continuously decreasing after 1991 as a change point. And the statistical tests such as t-test and Wilcoxon rank sum test reveals that the average annual precipitation of after 1991 is less than that of before 1991 at 10% significance level. And the mean gnu성 precipitation In Kyongnam districts is also continuously decreasing after 1991 same as Pusan.

• 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.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.10-19
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• 1994

We study the change-point problem in the context of system reliability models. The maximum likelihood estimators are obtained based on the Jelinski and Moranda model. To find the approximate distribution of the change-point estimator, we suggest of parametric bootstrap method in which the estimators are substituted in the assumed model. Through an example we illustrate the proposed method.

• Asia pacific journal of information systems
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• v.11 no.4
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• pp.99-111
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• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• Journal of Korean Society for Quality Management
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• v.30 no.3
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• pp.195-202
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• 2002

The problem of trend change in the mean residual life is great Interest in the reliability and survival analysis. In this paper, a new test statistic for testing whether or not the mean residual life changes its trend Is developed. It is assumed that neither the change point nor the proportion at which the trend change occurs is known. The asymptotic null distribution of test statistic is established and asymptotic critical values of the asymptotic null distribution is obtained. Monte Carlo simulation is used to compare the proposed test with previously known tests.

• Kyungpook Mathematical Journal
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• v.46 no.1
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• pp.145-152
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• 2006

In this paper, we study a class of strongly nonlinear implicit complementarity problems in the setting of Hilbert spaces H (not necessarily Hilbert lattices). By using the property of the projection and a suitable change of variables, we establish the equivalence between the strongly nonlinear implicit complementarity problem and the fixed point problem in H. Moreover, we use this equivalence and the fixed point theorem of Boyd and Wong to prove the existence and uniqueness of solutions for the strongly nonlinear implicit complementarity problem in H.

• Convergence Security Journal
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• v.8 no.4
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• pp.115-120
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• 2008

Accurate predictions of software release times, and estimation of the reliability and availability of a software product require quantification of a critical element of the software testing process： Change-point problem. In this paper, exponential (Goel-Okumoto) model was reviewed, proposes the percentile change-point problem, which maked out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on SSE statistics, for the sake of efficient model, was employed. Using NTDS data, The numerical example of percentilechange-point problemi s presented.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.187-199
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• 2003

We deal with the problem of testing a sequence of independent normal random variables with constant, known or unknown, variance for no change in mean versus alternatives with a single change-point. Various tests based on the likelihood ratio and recursive residuals, score statistics and cusums are studied. Proposed tests are modified version of Buckley's cusum statistics. A comparison study of various change-point test statistics is done by Monte Carlo simulation with S-plus software.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.427-436
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• 2000

The prediction of stock price index is a very difficult problem because of the complexity of the stock market data it data. It has been studied by a number of researchers since they strong1y affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain Intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network (BPN). Fina1ly, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.