• 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.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.251-260
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• 2002

The aim of this paper is to consider of detecting the location, the jump size and the number of change-points in regression functions by using the local linear fit which is one of nonparametric regression techniques. It is obtained the asymptotic properties of the change points and the jump sizes. and the correspondin grates of convergence for change-point estimators.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.185-193
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• 2002

The purpose of this paper is to suggest a method for detecting the linear regression change-points or variance change-points in regression model by the combination of Schwarz information criteria. The advantage of the suggested method is to detect change-points more detailed when one compares the suggest method with Chen (1998)'s method.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.237-246
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• 2012

Bayesian methods have been recently used to identify multiple change-points. However, the studies for small data are limited. This paper suggests the Bayesian noncentral t distribution change-point model for small data, and applies the Metropolis-Hastings-within-Gibbs Sampling algorithm to the proposed model. Numerical results of simulation and real data show the performance of the new model in terms of the quality of the resulting estimation of the numbers and positions of change-points for small data.

• Journal of Pharmaceutical Investigation
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• v.8 no.3
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• pp.24-31
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• 1978

The relationship between temperature and apparent logarithmic hardness of ointments were clearly demonstrated. The followings were obtained as the results: 1. When the ointment base was mixed with additives and heated or cooled at various temperatures, the apparent logarithmic hardness in the first trend before reaching the critical point is subject to change mainly by the contents of the additive while in the secondary trend after reaching the critical point is subject to change mainly by the temperature. 2. No Change in the critical point was observed at different temperatures. It is assumed that the crittical point of such ointment bases has no relationship with temperatures and that the critical point itself should rather depend on the physicochemical properties of the additives.

• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.73-73
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• 2010

The purpose of this study is to analyze illustrations, contents, and experiment in 6 kinds of science textbook from the 9th grade covering the phase change of the Moon (on the phase change of the Moon in six 9th grade science textbook) and to suggest coherent and effective contents and frame of the science textbook. Hence, the researcher decided the study problem. The study problems are as follows; 'Are the illustrations in the science textbook presented to help understand the phase change of the Moon depending on the position of the observer?', 'Does the contents of the book clearly mention the phase change of the Moon?', 'Can students understand the phase change of the Moon through the experiments in the science textbook?', 'Do illustrations, contents, and experiment of the science textbook consistently explain phase change of the Moon?'. 10 persons (9graduate students including the researcher) took part in this study. All things unanimously agreed upon by all participants were reflected in the results. The results are as follows. First, the universe observer's view point is mixed with the earth observer's view in illustration of these science textbook regarding the phase change of the Moon. Moreover, illustrations of some textbooks are presented with such words as 'sunrise', 'midnight' and consequently contain too much contents. Second, the contents of the science textbook concerning the phase change of the Moon is not described clearly. In addition, they don't give clear and detailed explanations for the reason of the phase change of the Moon. Third, all of the textbooks, except one textbook, describe the experiment regarding the phase change of the Moon with the earth observer's view point but don't specifically mention that the view point is that of the earth observer's view point. Fourth, illustrations, contents, and experiments in the science textbook don't coherently explain the phase change of the Moon. In addition, it is confirmed through the process of the result analysis that the described contents in the science curriculum is not well constructed or logical.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate 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 network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.23-26
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• 2000

It is noted that many econometric time series have long-memory properties. A long-memory process, or strongly dependent process, is characterized by hyperbolic decaying autocorrelations and unbounded spectral density at the origin. Since the long-memory property can be observed by data obtained from rather a long period, there is some possibility of parameter change in the process. In this paper, we consider the estimation of change-point when there is a change in the variance of a long-memory process. The estimator is based on some reasonable statistic and the consistency is shown using Taqqu's strong reduction theorem

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.687-695
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• 2009

The Bayesian multiple change-point estimation has been applied to the daily means of ozone and PM10 data in Seoul for the period 1999. We focus on the detection of multiple change-points in the ozone and PM10 bivariate vectors by evaluating the posterior probabilities and Bayesian information criterion(BIC) using the stochastic approximation Monte Carlo(SAMC) algorithm. The result gives 5 change-points of mean vectors of ozone and PM10, which are related with the seasonal characteristics.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.491-501
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• 2006

We consider a multiple change point model with autoregressive conditional heteroscedasticity (ARCH). The model assumes that all or the part of the parameters in the ARCH equation change over time. The occurrence of the change points is modelled as the discrete time Markov process with unknown transition probabilities. The model is estimated by Markov chain Monte Carlo methods based on the approach of Chib (1998). Simulation is performed using a variant of perfect sampling algorithm to achieve the accuracy and efficiency. We apply the proposed model to the simulated data for verifying the usefulness of the model.