• Title/Summary/Keyword: Change Point Model

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Bayesian Multiple Change-Point for Small Data (소량자료를 위한 베이지안 다중 변환점 모형)

  • Cheon, Soo-Young;Yu, Wenxing
    • 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.

Nonparametric Change-point Estimation with Rank and Mean Functions in a Location Parameter Change Model (위치모수 변화 모형에서 순위함수와 평균함수를 이용한 비모수적 변화점 추정)

  • Kim, Jae-Hee;Lee, Kyoung-Won
    • 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.

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Kolmogorov-Smirnov Type Test for Change with Sample Fourier Coefficients

  • Kim, Jae-Hee
    • Journal of the Korean Statistical Society
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    • v.25 no.1
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    • pp.123-131
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    • 1996
  • The problerm of testing for a constant mean is considered. A Kolmogorov-Smirnov type test using the sample Fourier coefficients is suggested and its asymptotic distribution is derived. A simulation study shows that the proposed test is more powerful than the cusum type test when there is more than one change-point or there is a cyclic change.

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Bayesian Multiple Change-Point Estimation of Multivariate Mean Vectors for Small Data

  • Cheon, Sooyoung;Yu, Wenxing
    • 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.

Multiple change-point estimation in spectral representation

  • Kim, Jaehee
    • Communications for Statistical Applications and Methods
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    • v.29 no.1
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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.

Using Classification function to integrate Discriminant Analysis, Logistic Regression and Backpropagation Neural Networks for Interest Rates Forecasting

  • Oh, Kyong-Joo;Ingoo Han
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2000.11a
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    • pp.417-426
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    • 2000
  • This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

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A Change Point Problem in the Regression Model When the Errors are Correlated

  • Cho, Sinsup;Cho, Kwan Ho;Song, Moon Sup
    • Journal of Korean Society for Quality Management
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    • v.16 no.2
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    • pp.68-81
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    • 1988
  • Testing procedures for a detection of change point in the regression model with correlated errors are discussed. A Bayesian approach is adopted and applied to a regression model with errors following an AR(1) model.

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NONPARAMETRIC ESTIMATION OF THE VARIANCE FUNCTION WITH A CHANGE POINT

  • Kang Kee-Hoon;Huh Jib
    • 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.

Nonparametric Estimation of Discontinuous Variance Function in Regression Model

  • Kang, Kee-Hoon;Huh, Jib
    • 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.

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Locating the Change Point of Mean Residual Life of Certain Life Distributions

  • Li, Xiaohu
    • International Journal of Reliability and Applications
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    • v.3 no.2
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    • pp.91-98
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    • 2002
  • A class of life distributions, whose mean residual life keeps stable at its earlier phase and then starts to decrease in time, is proposed to model the life of an element haying survived its burn-in. A strongly consistent estimator and a nonparametric testing procedure are developed to locate the occurrence of the change-point of the mean residual life. Finally, some numerical simulations are employed to be an illustration as well.

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