• 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

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.155-167
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• 2007

Knowing the time of the process change could lead to quicker identification of the responsible special cause and less process down time, and it could help to reduce the probability of incorrectly identifying the special cause. In this paper, we propose the maximum likelihood estimator (MLE) for the process change point when a control chart is used in monitoring the mean of a process in which the observations can be modeled as an AR(1) process plus an additional random error. The performance of the proposed MLE is compared to the performance of the built-in estimator when they are used in EWMA charts based on the residuals. The results show that the proposed MLE provides good performance in terms of both accuracy and precision of the estimator.

• 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.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.6
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• pp.962-971
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• 2004

This paper has dealt with thermo-physical properties of microencapsulated phase change material slurry as a latent heat storage material having a low melting point. The measured results of the thermo-physical properties of the test microencapsulated phase change material slurry, those are, density, specific heat, thermal conductivity and viscosity, were discussed for the temperature region of solid and liquid phases of the dispersion material (paraffin). The measurements of these properties of microencapsulated phase change material slurry have been carried out by using a specific-gravity meter, a water calorimeter, a differential scanning calorimeter(DSC), a transient hot wire method and rotating type viscometer, respectively. It was clarified that the additional properties law could be applied to the estimation of the density and specific heat of microencapsulated phase change material slurry and also the Euckens equation could be applied to the estimation of the thermal conductivity of this slurry.

• 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.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.

• Convergence Security Journal
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• v.8 no.4
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• pp.115-120
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• 2008

Accurate predictions of software release times, and estimation of the reliability and availability of a software product require quantification of a critical element of the software testing process： Change-point problem. In this paper, exponential (Goel-Okumoto) model was reviewed, proposes the percentile change-point problem, which maked out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on SSE statistics, for the sake of efficient model, was employed. Using NTDS data, The numerical example of percentilechange-point problemi s presented.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup3
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• pp.5-10
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• 2016

Breast cancer, the second cause of cancer-related death after lung cancer and the most common cancer in women after skin cancer, is curable if detected in early stages of clinical presentation. Knowledge as to any age cut-off points which might have significance for prognostic groups is important in screening and treatment planning. Therefore, determining a change-point could improve resource allocation. This study aimed to determine if a change point for survival might exist in the age of breast cancer diagnosis. This study included 568 cases of breast cancer that were registered in Breast Cancer Research Center, Tehran, Iran, during the period 1986-2006 and were followed up to 2012. In the presence of curable cases of breast cancer, a change point in the age of breast cancer diagnosis was estimated using a mixture survival cure model. The data were analyzed using SPSS (versions 20) and R (version 2.15.0) software. The results revealed that a change point in the age of breast cancer diagnosis was at 50 years age. Based on our estimation, 35% of the patients diagnosed with breast cancer at age less than or equal to 50 years of age were cured while the figure was 57% for those diagnosed after 50 years of age. Those in the older age group had better survival compared to their younger counterparts during 12 years of follow up. Our results suggest that it is better to estimate change points in age for cancers which are curable in early stages using survival cure models, and that the cure rate would increase with timely screening for breast cancer.

• 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.

• Journal of the Korean Statistical Society
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• v.33 no.1
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• pp.59-78
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• 2004

A regression function in generalized linear model may have a discontinuity/change point at unknown location. In order to estimate the location of the discontinuity point and its jump size, the strategy is to use a nonparametric approach based on one-sided kernel weighted local-likelihood functions. Weak convergences of the proposed estimators are established. The finite-sample performances of the proposed estimators with practical aspects are illustrated by simulated examples.