• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.863-874
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• 2021

In this paper, we propose a post-selection inference procedure for the fused lasso signal approximator (FLSA). The FLSA finds underlying sparse piecewise constant mean structure by applying total variation (TV) semi-norm as a penalty term. However, it is widely known that this convex relaxation can cause asymptotic inconsistency in change points detection. As a result, there can remain false change points even though we try to find the best subset of change points via a tuning procedure. To remove these false change points, we propose a post-selection inference for the FLSA. The proposed procedure applies a permutation test based on CUSUM statistic. Our post-selection inference procedure is an extension of the permutation test of Antoch and Hušková (2001) which deals with single change point problems, to multiple change points detection problems in combination with the FLSA. Numerical study results show that the proposed procedure is better than naïve z-tests and tests based on the limiting distribution of CUSUM statistics.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.193-199
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• 2005

A simple method is proposed to detect the number of change points with jump discontinuities in nonparamteric regression functions. The proposed estimators are based on a local linear regression fit by the comparison of left and right one-side kernel smoother. Also, the proposed methodology is suggested as the test statistic for detecting of change points and the direction of jump discontinuities.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.31-38
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• 2003

A simple method is proposed to detect the number of change points and test the location and size of multiple change points with jump discontinuities in an otherwise smooth regression model. The proposed estimators are based on a local linear regression fit by the comparison of left and right one-side kernel smoother. Our proposed methodology is explained and applied to real data and simulated data.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.101-111
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• 1994

In this paper, we can see that software reliability model has been improved by considering multiple change-points. The condition for the existence of maximum likelihood estimate of the initial error content of a program is given and the maximum likelihood estimations of multiple change-points are derived. We assess the performance of our multiple change-points model on numerical applicaiton.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.423-432
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• 2012

This paper is concerned with the detection of multiple change-points in linear regression models. The proposed procedure relies on the local estimation for global change-point estimation. We propose a multiple change-point estimator based on the local least squares estimators for the regression coefficients and the split measure when the number of change-points is unknown. Its statistical properties are shown and its performance is assessed by simulations and real data applications.

• Journal of applied mathematics & informatics
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• v.8 no.2
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• pp.641-649
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• 2001

A procedure of detecting change-points is considered with split linear fitting idea from Hall and Titterington(1992). At each given point, left, central and right linear fits are compared to detect the discontinuities or change-points. A simulation study is done with various types of change models and shows that the suggested technique can be a flexible data-analytic tool.

• 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.

• Korean Management Science Review
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• v.18 no.1
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• pp.119-133
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• 2001

It is important to find the equilibrium level of real interest rate for it affects real and financial sector of economy. However, it is difficult to find the equilibrium level because like the most macroeconomic model the real interest model has parameter instability problem caused by structural change and it is supported by various theories and definitions. Hence, in order to cover these problems structural change detection model of real interest rate is developed to combine the real interest rate equilibrium model and the procedure to detect structural change points. 3 equations are established to find various effects of other interest-related macroeconomic variables and from each equation, structural changes are found. Those structural change points are consistent with common expectation. Oil Crisis (December, 1987), the starting point of Economic Stabilization Policy (January, 1982), the starting point of capital liberalization (January, 1988), the starting and finishing points of Interest deregulation (January, 1992 and December, 1994), Foreign Exchange Crisis (December, 1977) are detected as important points. From the equation of fisher and real effects, real interest rate level is estimated as 4.09% (October, 1988) and dependent on the underlying model, it is estimated as 0%∼13.56% (October, 1988), so it varies so much. It is expected that this result is connected to the large scale simultaneous equations to detect the parameter instability in real time, so induces the flexible economic policies.

• Journal of The Geomorphological Association of Korea
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• v.23 no.1
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• pp.47-60
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• 2016

In this study, datum points measurement have been collected and then weather data have been analyzed to figure out erosion and deposition environmental change around Halmi-island, Baramarae. First of all, it was difficult to analyze geomorphological change which is caused by climate change because of quite short term of collection period of data. However, differences in spatial distribution of erosion and deposition have locally been shown. In all season, the wind is blowing in north and north-west direction mostly except in summer which is shifted to south direction. However, since its ratio which are above 5m/s is much lower than the north and north-west wind, its effect on geomorphological process is very tiny. In order to look at a tendency of erosion and deposition environmental change around Baramarae Halmi-island, the periphery of Halmi-island was classified to east and west part, then accumulated erosion and deposition values have been calculated. As a result, generally, the datum points are located in the west part which are mostly depositional sites. On the other hand, the datum points are located in east part showed the dominant erosion patterns.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.37-39
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• 2000

The purpose of this paper is to present a neural network model based on change-point detection for the prediction of the U.S. Treasury Securities. Interest rates have been studied by a number of researchers since they strongly affect other economic and financial parameters. Contrary to other chaotic financial data, the movement of interest rates has a series of change points due to the monetary policy of the U.S. government. 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 interest rates forecasting. The proposed model consists of three stages. The first stage is to detect successive change points in the interest rates dataset. The second stage is to forecast the change-point group with the backpropagation neural network (BPN). The final stage is to forecast the output with BPN. This study then examines the predictability of the integrated neural network model for interest rates forecasting using change-point detection.