• Journal of the Korean Statistical Society
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• v.35 no.1
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• pp.1-23
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• 2006

In this paper we consider an estimation of the discontinuous variance function in nonparametric heteroscedastic random design regression model. We first propose estimators of the change point in the variance function and then construct an estimator of the entire variance function. We examine the rates of convergence of these estimators and give results for their asymptotics. Numerical work reveals that using the proposed change point analysis in the variance function estimation is quite effective.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1091-1098
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• 2006

Park et al.(2005) and Choi et al.(2006) studied quick detection of variance change point for time series data in progress. For efficient detection they used moving variance ratio equipped with two tuning parameters; information tuning parameter p and lag tuning parameter q. In this paper, the moving variance ratio is studied under harsh conditions.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.27-40
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• 2001

The purpose of this study is to test a multiple change point in the regression model with the passage of time, using the estimated residual variance figure suggested by Gasser, Sroka and Jennen - Steinmez (GSJS). As a result of the simulation, it is showed that there is a jump change of the estimated residual variance figure at that time of change point. The way to analyse a intuitive multiple change point through graphics is more effective and accurate than any other existing ways.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.103-108
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• 2002

We consider an estimation of discontinuous variance function in nonparametric heteroscedastic random design regression model. We first propose estimators of a change point and jump size in variance function and then construct an estimator of entire variance function. We examine the rates of convergence of these estimators and give results on their asymptotics. Numerical work reveals that the effectiveness of change point analysis in variance function estimation is quite significant.

• Journal of applied mathematics & informatics
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• v.7 no.2
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• pp.653-664
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• 2000

The problem of inference about the unknown change-point with a change in mean is considered. We suggest a nonparametric change-point estimator using window and prove its consistency when the errors are from the distribution with the mean zero and the common variance. a comparison study is done by simulation on the mean, the variance, and the proportion of matching the true change-points.

• Journal of Korean Society for Quality Management
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• v.17 no.2
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• pp.101-112
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• 1989

We investigate a procedure which detects the parameter change point in AR(1) by Bayesian Approach using Jeffrey prior, for example, coefficient change point, variance change point, coefficient and variance change point, etc. And we apply our procedure to the simulated data.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.289-300
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• 2005

In this article quick variance change point (VCP) detection problem for time series is considered. For this variance VCP detector equipped with tuning parameters is proposed. A major tool for the detector is moving variance ratio (MVR) which monitors variance change of a given time series. Tuning process of detector is investigated via simulation, which shows that tuning parameters are critical in achieving sensitivity and adaptiveness of detector.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.173-183
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• 2005

This paper studies quick detection of variance change point for iid data. For development of sensitive and adaptive variance change point detector, moving variance ratio is employed as a variance ratio estimator. It is shown that selection of tuning parameters of detector, (i.e., information and lag tuning parameters) is critical for detector to achieve desirable sensitivity and adaptiveness. Interestingly our simulation result reveals limitations of the commonly used change ratio against the previous day. Our results will provide useful insight when the detector is applied to time series data.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.111-120
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• 2016

In usual, the discontinuous variance function was estimated nonparametrically using a kernel type estimator with data sets split by an estimated location of the change point. Kang et al. (2000) proposed the Gasser-$M{\ddot{u}}ller$ type kernel estimator of the discontinuous regression function using the adjusted observations of response variable by the estimated jump size of the change point in $M{\ddot{u}}ller$ (1992). The adjusted observations might be a random sample coming from a continuous regression function. In this paper, we estimate the variance function using the Nadaraya-Watson kernel type estimator using the adjusted squared residuals by the estimated location of the change point in the discontinuous variance function like Kang et al. (2000) did. The rate of convergence of integrated squared error of the proposed variance estimator is derived and numerical work demonstrates the improved performance of the method over the exist one with simulated examples.