• 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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• 2004.10a
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• pp.61-61
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• 2004

• 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.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.589-603
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• 2014

When consecutive data follows different distributions(depending on the time interval) change-point detection infers where the changes occur first and then finds further inferences for each sub-interval. In this paper, we investigate the Bayesian detection of multiple change points. Utilizing the reversible jump MCMC, we can explore parameter spaces with unknown dimensions. In particular, we consider a model where the signal is a piecewise linear function. For the Bayesian inference, we propose a new Bayesian structure and build our own MCMC algorithm. Through the simulation study and the real data analysis, we verified the performance of our method.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.621-633
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• 2006

In this paper, we estimate multiple change-points when the data follow the normal distributions in the Bayesian way. Evolutionary Monte Carlo (EMC) algorithm is applied into general Bayesian model with variable-dimension parameters and shows its usefulness and efficiency as a promising tool especially for computational issues. The method is applied to the humidity data of Seoul and the final model is determined based on BIC.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.999-1008
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• 2012

A Bayesian multiple change-point model for small data is proposed for multivariate means and is an extension of the univariate case of Cheon and Yu (2012). The proposed model requires data from a multivariate noncentral $t$-distribution and conjugate priors for the distributional parameters. We apply the Metropolis-Hastings-within-Gibbs Sampling algorithm to the proposed model to detecte multiple change-points. The performance of our proposed algorithm has been investigated on simulated and real dataset, Hanwoo fat content bivariate data.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.8
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• pp.388-394
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• 2001

This paper presents how to determine the optimal operating points of Unified Power Flow controllers (UPFC) the line flow control of which can enhance system security level. In order to analyze the effect of these devices on the power system, the decoupled model has been employed as a mathematical model of UPFC for power flow analysis. The security index that indicates the level of congestion of transmission line has been proposed and minimized by iterative method. The sensitivity of objective function for control variables of and UPFC has been derived, and it represents the change in the security index for a given set of changes in real power outputs of UPFC. The proposed algorithm with sensitivity analysis gives the optimal set of operating points of multiple UPECs that reduces the index or increases the security margin and Marquart method has been adopted as an optimization method because of stable convergence. The algorithm is verified by the 10-unit 39-bus New England system that includes multiple FACTS devices. The simulation results show that the power flow congestion can be relieved in normal state and the security margin can be guaranteed even in a fault condition by the cooperative operation of multiple UPECs.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.8
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• pp.55-64
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• 1999

In this paper, new gamut-mapping algorithm (GMA) that utilizes variable anchor points (center of gravity on the luminance axis) is proposed. The proposed algorithm increases luminance range, which is reduced from conventional gamut mapping toward an anchor point. In this process, this algorithm utilizes multiple anchor points with constant slopes to both reduce a sudden color change on the gamut boundary of the printer and to maintain a uniform color change during the mapping process. Accordingly, the proposed algorithm can reproduce high quality images with low-cost color devices.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1147-1154
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• 2016

In this paper, we numerically compare two penalized least square methods, the ${\ell}_0$-penalized method and the fused lasso regression (FLR, ${\ell}_1$ penalization), in finding multiple change points of a signal. We find that the ${\ell}_0$-penalized method performs better than the FLR, which produces many false detections in some cases as the theory tells. In addition, the computation of ${\ell}_0$-penalized method relies on dynamic programming and is as efficient as the FLR.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.300-310
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• 2001

In this paper, new gamut-mapping algorithm (GMA) that utilizes variable anchor points (center of gravity on the luminance axis) is proposed. The proposed algorithm increases luminance range, which is reduced from conventional gamut mapping toward an anchor point. In this process, this algorithm utilizes multiple anchor points with constant slopes to both reduce a sudden color change on the gamut boundary of the printer and to maintain a uniform color change during the mapping process. Accordingly, the proposed algorithm can reproduce high quality images with low-cost color devices.