• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.349-357
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• 2005

We suggest a functional technique with loess smoothing for estimating the change-point when there is one change-point in the mean model. The proposed change-point estimator is consistent. Simulation study shows a good performance of the proposed change-point estimator in comparison with other parametric or nonparametric change-point estimators.

• Asia pacific journal of information systems
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• 제11권4호
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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 the Korean Statistical Society
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• 제28권3호
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• pp.359-370
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• 1999

We considered a change-point hazard rate model generalizing constant hazard rate model. This type of model is very popular in the sense that the Weibull and exponential distributions formulating survival time data are the special cases of it. Maximum likelihood estimation and the asymptotic properties such as the consistency and its limiting distribution of the change-point estimator were discussed. A parametric bootstrap method for finding confidence intervals of the unknown change-point was also suggested and the proposed method is explained through a practical example.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
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• pp.235-241
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• 2000

This article suggests integrated neural network models for the interest rate forecasting using change point detection. The basic concept of proposed model is to obtain intervals divided by change point, to identify them as change-point groups, and to involve them in interest rate forecasting. the proposed models consist of three stages. The first stage is to detect successive change points in interest rate dataset. The second stage is to forecast change-point group with data mining classifiers. The final stage is to forecast the desired output with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. This article is then to examine the predictability of integrated neural network models for interest rate forecasting using change-point detection.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
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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.

• Journal of the Korean Data and Information Science Society
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• 제9권1호
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• pp.1-9
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• 1998

영과잉-포아송모형에서 변화시점이 있는 경우, 우도비 검정통계량을 이용하여 변화 시점의 유 무에 대한 가설을 검정하였다. 또한 적률 및 최우추정법을 이용하여 변화 시점과 몇가지 흥미있는 모수들을 추정하여 보았다. 이들 추정량을 비교하기 위하여 경험적인 평균제곱오차를 이용하였다. 변화시점이 있는 영과잉-포아송 모형과 변화시점이 없는 포아송 모형의 실례를 자료를 중심으로 설명하였다.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.327-336
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• 2000

We are mainly interested in hazard rate changes which are usually occur in survival times of manufactured products or patients. We may expect early failures with one hazard rate and next another hazard rate. For this type of data we apply a hazard rate change-point model and estimate the unkown time point to improve the model adequacy. We introduce change-point logistic model to the discrete time hazard rates. The MLEs are obtained routinely and we also explain the suggested model through a dataset of survival times.

• 대기
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• 제18권2호
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• pp.111-120
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• 2008

This study presents a change-point in the 30 years (1976-2005) time series of the annual and the heavy precipitation characteristics (amount, days and intensity) averaged over South Korea using Bayesian approach. The criterion for the heavy precipitation used in this study is 80 mm/day. Using non-informative priors, the exact Bayes estimators of parameters and unknown change-point are obtained. Also, the posterior probability and 90% highest posterior density credible intervals for the mean differences between before and after the change-point are examined. The results show that a single change-point in the precipitation intensity and the heavy precipitation characteristics has occurred around 1996. As the results, the precipitation intensity and heavy precipitation characteristics have clearly increased after the change-point. However, the annual precipitation amount and days show a statistically insignificant single change-point model. These results are consistent with earlier works based on a simple linear regression model.

• 응용통계연구
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• 제17권2호
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• pp.281-301
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• 2004

본 논문에서는 일변량 정규분포를 따르는 확률변수의 관측치열에 대한 변화점 문제(change point problem)를 고찰한다. 변화점의 존재유무, 그리고 만일 변화점이 존재한다면 어떠한 유형으로 발생했는지 즉, 변화점 발생 이후로 평균만 변화, 분산만 변화, 또는 평균과 분산 모두가 변화했는지를 밝힌다. 가능한 여러 유형의 변화모형들 가운데 최적의 모형을 선택하기 위해 베이지안 모형선택 기법을 이용하고, 선택된 모형에 내재된 모수를 추정 하기 위해 메트로폴리스-혜스팅스 알고리 즘을 포함한 깁스샘플링 을 이용한다. 이러한 방법론은 모의실험을 통해 검토되고, 또한 서울지역의 겨울철 평균기온 자료에 적용된다.

• Journal of the Korean Data and Information Science Society
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• 제13권1호
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• pp.165-175
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• 2002

We consider the problem of estimating the change-point in mean change model with the one change-point. Lombard (1987) suggested change-point estimation based on score functions. Gombay and Huskova (1998) derived a class of change-point estimators with the score function of rank. Various change-point estimators with the log score functions of ranks are suggested and compared via simulation.