• The Korean Journal of Applied Statistics
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• v.4 no.1
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• pp.33-45
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• 1991

In the analysis of changepoint model the situation where single observation is taken at each time point has been considered. In an effort to extend this to the general situation, we may consider the changepoint model with more than one observation at each time point. These tests are developed without assuming any particular form for the underlying distribution, we propose the one-sided and two-sided nonparametric tests by extending the tests that have been considered in the changepoint model with single observation at each time point and obtain their asymptotic null distributions. We compare the empirical powers among the extended changepoint tests under one-sided or two-sided alternatives. We also compare the powers of the extended changepoint tests with those of the original test via the Monte Carlo simulation.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.499-512
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• 2022

Intervention measures have been implemented worldwide to reduce the spread of the COVID-19 outbreak. The COVID-19 outbreak has occured in several waves of infection, so this paper is divided into three groups, namely those countries who have passed the pandemic period, those countries who are still experiencing a single-wave pandemic, and those countries who are experiencing a multi-wave pandemic. The purpose of this study is to develop a multi-wave Richards model with several changepoint detection methods so as to obtain more accurate prediction results, especially for the multi-wave case. We investigated epidemiological trends in different countries from January 2020 to October 2021 to determine the temporal changes during the epidemic with respect to the intervention strategy used. In this article, we adjust the daily cumulative epidemiological data for COVID-19 using the logistic growth model and the multi-wave Richards curve development model. The changepoint detection methods used include the interpolation method, the Pruned Exact Linear Time (PELT) method, and the Binary Segmentation (BS) method. The results of the analysis using 9 countries show that the Richards model development can be used to analyze multi-wave data using changepoint detection so that the initial data used for prediction on the last wave can be determined precisely. The changepoint used is the coincident changepoint generated by the PELT and BS methods. The interpolation method is only used to find out how many pandemic waves have occurred in given a country. Several waves have been identified and can better describe the data. Our results can find the peak of the pandemic and when it will end in each country, both for a single-wave pandemic and a multi-wave pandemic.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.35-46
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• 1994

We propose the nonparametric rank-like test for the location parameter in the bivariate changepoint model. Empirical powers between the parametric test and nonparametric test are compared. These results show that rank-like test is better than parametric method except bivariate normal null distribution. The point estimators for the changepoint are also compared by the empirical mean squared errors.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.1-16
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• 2002

Since changepoint identification is important in many data analysis problem, we wish to make inference about the locations of one or more changepoints of the sequence. We consider the Bayesian nonparameteric inference for multiple changepoint problem using a Bayesian segmentation procedure proposed by Yang and Kuo (2000). A mixture of products of Dirichlet process is used as a prior distribution. To decide whether there exists a single change or not, our approach depends on nonparametric Bayesian Schwartz information criterion at each step. We discuss how to choose the precision parameter (total mass parameter) in nonparametric setting and show that the discreteness of the Dirichlet process prior can ha17e a large effect on the nonparametric Bayesian Schwartz information criterion and leads to conclusions that are very different results from reasonable parametric model. One example is proposed to show this effect.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.319-327
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• 1999

Zero-Inflated Poisson distributions have been widely used for defect-free products in manufacturing processes. It is very interesting to check the shift after the unknown changepoint. If the detectives are caused by the two different types of factor, we should use bivariate zero-inflated model. In this paper, likelihood ratio tests were used to detect the shift of changes after the changepoint. Some inferences for the parameters in this model were made.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.483-496
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• 2005

We consider the power law process which is assumed to have multiple changepoints. We propose a binary segmentation procedure for locating all existing changepoints. We select one model between the no-changepoints model and the single changepoint model by the Bayes factor. We repeat this procedure until no more changepoints are found. Then we carry out a multiple test based on the Bayes factor through the intrinsic priors of Berger and Pericchi (1996) to investigate the system behaviour of failure times. We demonstrate our procedure with a real dataset and some simulated datasets.

• The Journal of Internal Korean Medicine
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• v.42 no.6
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• pp.1255-1268
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• 2021

Objectives: This study aimed to analyze the healthcare utilization behavior of patients visiting the department of Korean internal medicine in the Korean medicine hospital of Wonkwang University in Jeon-ju from July 2018 to June 2021. Methods: We retrospectively analyzed the medical records of 26,108 patients and sorted the data by period, month, visiting types, new or returning types, sex, and age group. IBM SPSS 26.0 and the R 4.05 'changepoint' package were used with various statistical methods, such as Independent t-test, Mann-Whitney test, Chi-square test, Simple regression analysis. The P-value was set at 0.05. Results and Conclusions: Females outnumbered males regardless of period, and the ratio of females fell after COVID-19. Regardless of visiting types, patients in their 50s, 60s, and 70s outrated any other age group. The average number of females among the returning patients decreased significantly after COVID-19, but did not in males. Outpatients under 10 and in their 10s decreased significantly after COVID-19, as did inpatients in their 40s and 60s. The average duration of hospitalization was extended significantly after COVID-19. The number of outpatients and inpatients decreased as time passed after COVID-19. We expect that the results of this study will be used as reference materials in analyzing the effects of COVID-19 on healthcare utilization.