• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.477-489
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• 1998

위험률 변화점모형에 대해 변화점의 최우추정을 고려하였다. 추정량의 점근분포 및 붓스트랩 분포의 성질을 알아보고 변화점의 신뢰구간을 제안한다. 변화점의 위치 및 변화점을 전후하여 위험률의 값에 따라 모의실험을 수행하고 포함확률을 조사하였다. 추정량의 점근분포가 매우 복잡하기 때문에 이를 직접 이용한 변화점의 통계적 추론이 매우 어려운 점을 감안할 때 제안된 방법은 바람직한 대안이 될 수 있다.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.279-293
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• 2000

This article suggests two change-point estimators which are modifications of Carlstein(1988) change-point estimators with rank functions and mean functions where there is one change-point in a mean function. A comparison study of Carlstein(1988) estimators and proposed estimators is done by simulation on the mean, the MSE, and the proportion of matching true change-point.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.561-572
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• 2019

We study false discovery rate segmentation (FDRSeg) and simultaneous multiscale change-point estimator (SMUCE) methods for multiscale multiple change-point estimation, and compare empirical behavior via simulation. FSRSeg is based on the control of a false discovery rate while SMUCE used for the multiscale local likelihood ratio tests. FDRSeg seems to work best if the number of change-points is large; however, FDRSeg and SMUCE methods can both provide similar estimation results when there are only a small number of change-points. As a real data application, multiple change-points estimation is done with the well-log data.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1253-1262
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• 2016

When there exists a change-point in hazard function, it should be estimated for exact parameter or hazard estimation. In this research, we compare the hazard change-point estimators. Matthews and Farewell (1982) parametric change-point estimator is based on the likelihood and Zhang et al. (2014) nonparametric estimator is based on the Nelson-Aalen cumulative hazard estimator. Simulation study is done for the data from exponential distribution with one hazard change-point. The simulated data generated without censoring and the data with right censoring are considered. As real data applications, the change-point estimates are computed for leukemia data and primary biliary cirrhosis data.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.573-589
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• 2023

In a piecewise linear trend model, the change points coincide with the mean change points of the first differenced time series. Therefore, by detecting the mean change points of the first differenced time series, one can estimate the change points of the piecewise linear trend model. In this paper, based on this fact, a method is proposed for detecting change points of the piecewise linear trend model using the simple moving average of the first differenced time series rather than estimates of the slope or residuals. Our Monte Carlo simulation experiments show that the proposed method performs well in estimating the number of change points not only when the error terms in the piecewise linear trend model are independent but also when they are serially correlated.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.63-75
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• 2022

This paper proposes the so-called MOSUM-based sparse projection method for change points detection in high-dimensional time series. Our method is inspired by Wang and Samworth (2018), however, our method improves their method in two ways. One is to find change points all at once, so it minimizes sequential error. The other is localized so that more robust to the mean changes offsetting each other. We also propose data-driven threshold selection using block wild bootstrap. A comprehensive simulation study shows that our method performs reasonably well in finite samples. We also illustrate our method to stock prices consisting of S&P 500 index, and found four change points in recent 6 years.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.281-301
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• 2004

In this paper we consider the change point problem in a sequence of univariate normal observations. We want to know whether there is any change point or not. In case a change point exists, we will identify its change type. Namely, it can be a mean change, a variance change, or both the mean and variance change. The intrinsic Bayes factors of Berger and Pericchi (1996, 1998) are used to find the type of optimal change model. The Gibbs sampling including the Metropolis-Hastings algorithm is used to estimate all the parameters in the change model. These methods are checked via simulation and applied to the winter average temperature data in Seoul.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.163-175
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• 1998

The change of hazard rates at some unknown time point has been the interest of many statisticians. But it was restricted to the constant hazard rates which correspond to the exponential distribution. In this paper we generalize the change-point model in which any specific functional forms of hazard rates are net assumed. The assumed model includes various types of changes before and after the unknown time point. The Nelson estimator of cumulative hazard function is introduced. We estimate the change-point maximizing slope changes of Nelson estimator. Consistency and asymptotic distribution of bootstrap estimator are obtained using the martingale theory. Through a Monte Carlo study we check the performance of the proposed method. We also explain the proposed method using the Stanford Heart Transplant Data set.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.569-579
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• 2013

In the field of single-molecule spectroscopy, it is essential to analyze luminescence Intensity changes that result from a single molecule. With the CdSe/ZnS core-shell structured quantum dot photon emission data Bayesian multiple change-point estimation is done with the gamma prior for Poisson parameters and truncated Poisson distribution for the number of change-points.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.116-116
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• 2010

단일 양자점의 특성 분석 및 이를 활용한 단광자 광원 등으로의 응용에 있어서 표면밀도 및 크기 등이 의도대로 조절된 양자점 성장이 필수적이며, 이와 관련하여 근적외선 파장 영역에서 발광 성분을 갖는 InGaAs/GaAs 양자점 시료를 MEE (Migration Enhanced Epitaxy) 기법으로 성장하였다. 이 때, 30 초 120 초 사이의 migration enhancing time 변화에 의하여 약 $350\;QDs/{\mu}m^2$에서 $3\;QDs/{\mu}m^2$ 사이의 범위로 양자점 표면 밀도가 조절되었으며 양자점의 크기도 변화하는 것을 확인하였다. 별도로 capping layer를 성장하지 않은 양자점 층에 대한 AFM 측정을 통하여 양자점의 크기를 예측하였으나, 실제 시료의 양자점 크기는 capping layer 성장시의 온도 및 압력에 따른 영향이나 물질 조성의 불균일성 등으로 인해 달라질 수 있으므로 비파괴 검사방법인 광발광 측정으로써 실제 양자점의 특성을 검증할 필요성이 존재한다. 먼저 양자점의 크기가 커짐에 따라 기저상태의 에너지 밴드갭 크기가 감소하는 경향이 있음을 확인하였다. 이는 양자점이 클수록 양자구속 효과가 작아지는 일반적인 경향과 일치한다. 또한, 양자점의 크기 차이에 따른 기저상태 및 고차 여기 상태의 에너지 밴드갭 차이의 변화 경향을 분석하였다. 일반적으로 양자점의 크기가 줄어들면 양자구속효과 또한 빠르게 증가하다가 결국에는 에너지 장벽(barrier)의 에너지 준위에서 포화상태에 도달하게 된다. 이러한 양자점 크기에 따른 양자구속효과 크기의 변화는 고차 여기 상태일수록 더욱 빠르며, 결국에는 양자 구속효과가 없어지는 상태(unbound exciton)에 이르기도 한다. 따라서 기저상태의 에너지 밴드갭은 양자점이 커짐에 따라 단조감소 경향을 보이나, 변화율의 차이 때문에 기저상태와 1차 여기상태의 에너지 차이인 level spacing 값은 단조감소 경향이 아닌 종 모양의 경향성을 보이며 측정 결과 또한 이와 일치하였다. 이와 같이 migration enhancing time의 조절로 광자와 상호작용하는 실질적인 양자점의 크기가 의도대로 조절되었음을 비파괴 광측정법으로 확인하였다.