Bayesian Multiple Change-Point Estimation and Segmentation

Kim, Jae Hee; Cheon, Soo Young

doi:10.5351/csam.2013.20.6.439

Cited by 6 publications

(4 citation statements)

References 46 publications

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“…Therefore, the change point is estimated at 40. Several researches have been done with different approaches such that Bayesian analysis [28], likelihood ratio [29] and stochastic approximation [30] on the same dataset. In the comparison from the literature, a similar result has been carried out that the change point at 40 or year 1890 which is also met by Barry and Hartigan's approach.…”

Section: Resultsmentioning

confidence: 99%

Bayesian analysis of change point problems for time series data

Lee¹,

Ong²

2016

AIP Conference Proceedings

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Abstract. Alterations or sudden changes within a sequence of temporal observations always create disturbance to data analysis. The maneuver to detect this alterations or changes in any temporal data may allow researchers to identify the aberration in every block of segments. The Bayesian method proposed by Barry and Hartigan has greatly fitted the analysis of change point problems through product partition model. We study Bayesian analysis for change point problem with Markov sampling computation on British coal mine accident. The result provides accurate change point and posterior means estimation.

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Section: Resultsmentioning

confidence: 99%

Bayesian analysis of change point problems for time series data

Lee¹,

Ong²

2016

AIP Conference Proceedings

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“…However it would be difficult to apply the MC method for a nonparametric test since the distribution of population for the null hypothesis is too broad to choose a specific one. One may also note that Kim and Cheon (2013) applied the MC method to estimate the posterior distribution in the Bayesian analysis.…”

Section: Discussionmentioning

confidence: 99%

A note on the test for the covariance matrix under normality

Park

2018

CSAM

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In this study, we consider the likelihood ratio test for the covariance matrix of the multivariate normal data. For this, we propose a method for obtaining null distributions of the likelihood ratio statistics by the MonteCarlo approach when it is difficult to derive the exact null distributions theoretically. Then we compare the performance and precision of distributions obtained by the asymptotic normality and the Monte-Carlo method for the likelihood ratio test through a simulation study. Finally we discuss some interesting features related to the likelihood ratio test for the covariance matrix and the Monte-Carlo method for obtaining null distributions for the likelihood ratio statistics.

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“…In practice, AIC is used to find the best prediction model, whereas BIC is applied to choose the best model for further inferences. There is a wide range of applications of BIC including K-means clustering inverse regression (Ahn and Yoo, 2011), multiple change-points (Kim and Cheon, 2013), dynamic conditional correlation model (Kim, 2014), and growth mixture model (Lee et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Bayesian information criterion accounting for the number of covariance parameters in mixed effects models

Heo

Lee

Kim

2020

CSAM

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Schwarz's Bayesian information criterion (BIC) is one of the most popular criteria for model selection, that was derived under the assumption of independent and identical distribution. For correlated data in longitudinal studies, Jones (Statistics in Medicine, 30, 3050-3056, 2011) modified the BIC to select the best linear mixed effects model based on the effective sample size where the number of parameters in covariance structure was not considered. In this paper, we propose an extended Jones' modified BIC by considering covariance parameters. We conducted simulation studies under a variety of parameter configurations for linear mixed effects models. Our simulation study indicates that our proposed BIC performs better in model selection than Schwarz's BIC and Jones' modified BIC do in most scenarios. We also illustrate an example of smoking data using a longitudinal cohort of cancer patients.

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Bayesian Multiple Change-Point Estimation and Segmentation

Cited by 6 publications

References 46 publications

Bayesian analysis of change point problems for time series data

Bayesian analysis of change point problems for time series data

A note on the test for the covariance matrix under normality

Bayesian information criterion accounting for the number of covariance parameters in mixed effects models

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