• Communications for Statistical Applications and Methods
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• 제20권6호
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• pp.439-454
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• 2013

This study presents a Bayesian multiple change-point detection approach to segment and classify the observations that no longer come from an initial population after a certain time. Inferences are based on the multiple change-points in a sequence of random variables where the probability distribution changes. Bayesian multiple change-point estimation is classifies each observation into a segment. We use a truncated Poisson distribution for the number of change-points and conjugate prior for the exponential family distributions. The Bayesian method can lead the unsupervised classification of discrete, continuous variables and multivariate vectors based on latent class models; therefore, the solution for change-points corresponds to the stochastic partitions of observed data. We demonstrate segmentation with real data.

• Journal of the Korean Data and Information Science Society
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• 제28권4호
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• pp.889-898
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• 2017

In this research multiple change-points estimation for South Korean electricity generation data is considered. We analyze the South Korean electricity data via deterministically trending dynamic time series model with multiple structural changes in trends in a Bayesian approach. The number of change-points and the timing are unknown. The goal is to find the best model with the appropriate number of change-points and the length of the segments. A genetic algorithm is implemented to solve this optimization problem with a variable dimension of parameters. We estimate the structural change-points for South Korean electricity generation data and Nile River flow data additionally.

• 응용통계연구
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• 제32권4호
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• pp.561-572
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• 2019

본 연구는 다층적 다중변화점 추정법으로 FDRSeg 기법과 SMUCE 기법의 이론적 특성을 파악하고 모의실험을 통해 경험적 특성을 비교하고자한다. FDRSeg (False discovery rate segmentation)기법은 FDR 기반 조절을 하여 변화점을 추정하고 SMUCE (simultaneous multiscale change-point estimator) 기법은 국소우도함수 기반 다중 검정으로 변화점을 추정한다. 변화점의 개수가 작을경우에는 두 기법에 의한 추정능력이 비슷하다. 변화점 개수가 많을수록 FDRSeg 의 추정이 변화점 개수와 추정측도 면에서 더 좋은 편이다. 실제 데이터 분석으로 검층 주상도 데이터에 대해 각 기법으로 다중변화점 추정을 하고 비교한다.

• 응용통계연구
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• 제7권2호
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• pp.101-111
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• 1994

본 논문은 소프트웨어 신뢰도 모형에서 다중 변화점을 고려함으로서 미래의 관찰치에 대한 예측 성능을 높일 수 있는 새로운 모형에서 프로그램 에러수의 최우추정량이 유한일 조건을 제시하고, 변화점 추정 방법에 대해 논의한다. 또한, 제안된 모형의 타당성을 조사하기 위해 실제 예제를 통하여 모형 성능을 평가한다.

• Communications for Statistical Applications and Methods
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• 제19권3호
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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.

• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.127-150
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• 2022

We discuss multiple change-point estimation as edge detection in piecewise smooth functions with finitely many jump discontinuities. In this paper we propose change-point estimators using concentration kernels with Fourier coefficients. The change-points can be located via the signal based on Fourier transformation system. This method yields location and amplitude of the change-points with refinement via concentration kernels. We prove that, in an appropriate asymptotic framework, this method provides consistent estimators of change-points with an almost optimal rate. In a simulation study the proposed change-point estimators are compared and discussed. Applications of the proposed methods are provided with Nile flow data and daily won-dollar exchange rate data.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2006년도 춘계학술대회
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• pp.319-324
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• 2006

In this paper, we propose Bayesian procedure for the multiple change points analysis in a sequence of fractions nonconforming. We first compute the Bayes factor for detecting the existence of no change, a single change or multiple changes. The Gibbs sampler with the Metropolis-Hastings subchain is run to estimate parameters of the change point model, once the number of change points is identified. Finally, we apply the results developed in this paper to both a real and simulated data.

• 응용통계연구
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• 제22권4호
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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.

• 응용통계연구
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• 제26권4호
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• pp.569-579
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• 2013

단일 분자에서 발생한 발광의 세기 변화를 분석하는 문제는 단분자 분광학에서 반드시 필요하다. 본 연구에서는 카드뮴셀레나이드/황화아연의 중심-껍질 구조를 갖는 양자점에 대한 단분자 분광학 데이터에 대해 Poisson count data로서 베이지안 접근으로 모수에 대한 공액 감마분포와 변화점 개수에 대한 절단포아송 분포로 사전분포를 주고 다중변화점을 추정하였다.

• Communications for Statistical Applications and Methods
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• 제10권1호
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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.