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CSAM(Communications for Statistical Applications and Methods) update

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수록범위 : 1권1호(1994)~29권5호(2022) |수록논문 수 : 2,009
CSAM(Communications for Statistical Applications and Methods)
29권5호(2022년 09월) 수록논문
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SCOPUS

저자 : Faihatuz Zuhairoh , Dedi Rosadi

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 5호 발행 연도 : 2022 페이지 : pp. 499-512 (14 pages)

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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.

SCOPUS

저자 : Neill Smit , Lizanne Raubenheimer

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 5호 발행 연도 : 2022 페이지 : pp. 513-532 (20 pages)

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In this paper, the use of Bayes factors and the deviance information criterion for model selection are compared in a Bayesian accelerated life testing setup. In Bayesian accelerated life testing, the most used tool for model comparison is the deviance information criterion. An alternative and more formal approach is to use Bayes factors to compare models. However, Bayesian accelerated life testing models with more than one stressor often have mathematically intractable posterior distributions and Markov chain Monte Carlo methods are employed to obtain posterior samples to base inference on. The computation of the marginal likelihood is challenging when working with such complex models. In this paper, methods for approximating the marginal likelihood and the application thereof in the accelerated life testing paradigm are explored for dual-stress models. A simulation study is also included, where Bayes factors using the different approximation methods and the deviance information are compared.

SCOPUS

저자 : Siva G , Vishnu Vardhan R , Asha Kamath

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 5호 발행 연도 : 2022 페이지 : pp. 533-545 (13 pages)

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Collection of data on several variables, especially in the field of medicine, results in the problem of measurement errors. The presence of such measurement errors may influence the outcomes or estimates of the parameter in the model. In classification scenario, the presence of measurement errors will affect the intrinsic cum summary measures of Receiver Operating Characteristic (ROC) curve. In the context of ROC curve, only a few researchers have attempted to study the problem of measurement errors in estimating the area under their respective ROC curves in the framework of univariate setup. In this paper, we work on the estimation of area under the multivariate ROC curve in the presence of measurement errors. The proposed work is supported with a real dataset and simulation studies. Results show that the proposed bias-corrected estimator helps in correcting the AUC with minimum bias and minimum mean square error.

SCOPUS

저자 : Aejeong Jo , Dal Ho Kim

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 5호 발행 연도 : 2022 페이지 : pp. 547-559 (13 pages)

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For a chi-squared test, which is a statistical method used to test the independence of a contingency table of two factors, the expected frequency of each cell must be greater than 5. The percentage of cells with an expected frequency below 5 must be less than 20% of all cells. However, there are many cases in which the regional expected frequency is below 5 in general small area studies. Even in large-scale surveys, it is difficult to forecast the expected frequency to be greater than 5 when there is small area estimation with subgroup analysis. Another statistical method to test independence is to use the Bayes factor, but since there is a high ratio of data dependency due to the nature of the Bayesian approach, the low expected frequency tends to decrease the precision of the test results. To overcome these limitations, we will borrow information from areas with similar characteristics and pool the data statistically to propose a pooled Bayes test of independence in target areas. Jo et al. (2021) suggested hierarchical Bayesian pooling models for small area estimation of categorical data, and we will introduce the pooled Bayes factors calculated by expanding their restricted pooling model. We applied the pooled Bayes factors using bone mineral density and body mass index data from the Third National Health and Nutrition Examination Survey conducted in the United States and compared them with chi-squared tests often used in tests of independence.

SCOPUS

저자 : Sangho Park , Chanmin Kim

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 5호 발행 연도 : 2022 페이지 : pp. 561-589 (29 pages)

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When multiple classifications and regression trees are combined, tree-based ensemble models, such as random forest (RF) and Bayesian additive regression trees (BART), are produced. We compare the model structures and performances of various ensemble models for regression settings in this study. RF learns bootstrapped samples and selects a splitting variable from predictors gathered at each node. The BART model is specified as the sum of trees and is calculated using the Bayesian backfitting algorithm. Throughout the extensive simulation studies, the strengths and drawbacks of the two methods in the presence of missing data, high-dimensional data, or highly correlated data are investigated. In the presence of missing data, BART performs well in general, whereas RF provides adequate coverage. The BART outperforms in high dimensional, highly correlated data. However, in all of the scenarios considered, the RF has a shorter computation time. The performance of the two methods is also compared using two real data sets that represent the aforementioned situations, and the same conclusion is reached.

SCOPUS

저자 : Sora Park , Kipoong Kim , Hokeun Sun

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 5호 발행 연도 : 2022 페이지 : pp. 591-601 (11 pages)

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In gene set analysis with microarray expression data, a group of genes such as a gene regulatory pathway and a signaling pathway is often tested if there exists either differentially expressed (DE) or differentially co-expressed (DC) genes between two biological conditions. Recently, a statistical test based on covariance estimation have been proposed in order to identify DC genes. In particular, covariance regularization by hard thresholding indeed improved the power of the test when the proportion of DC genes within a biological pathway is relatively small. In this article, we compare covariance thresholding methods using four different regularization penalties such as lasso, hard, smoothly clipped absolute deviation (SCAD), and minimax concave plus (MCP) penalties. In our extensive simulation studies, we found that both SCAD and MCP thresholding methods can outperform the hard thresholding method when the proportion of DC genes is extremely small and the number of genes in a biological pathway is much greater than a sample size. We also applied four thresholding methods to 3 different microarray gene expression data sets related with mutant p53 transcriptional activity, and epithelium and stroma breast cancer to compare genetic pathways identified by each method.

SCOPUS

저자 : Su Hyeong Yang , Seung Jun Shin , Wooseok Sung , Choon Won Lee

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 5호 발행 연도 : 2022 페이지 : pp. 603-614 (12 pages)

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The naive Bayes classifier is one of the most straightforward classification tools and directly estimates the class probability. However, because it relies on the independent assumption of the predictor, which is rarely satisfied in real-world problems, its application is limited in practice. In this article, we propose employing sufficient dimension reduction (SDR) to substantially improve the performance of the naive Bayes classifier, which is often deteriorated when the number of predictors is not restrictively small. This is not surprising as SDR reduces the predictor dimension without sacrificing classification information, and predictors in the reduced space are constructed to be uncorrelated. Therefore, SDR leads the naive Bayes to no longer be naive. We applied the proposed naive Bayes classifier after SDR to build a recommendation system for the eyewear-frames based on customers' face shape, demonstrating its utility in the top-k classification problem.

SCOPUS

저자 : Minju Shin , Jae Keun Yoo

발행기관 : 한국통계학회 간행물 : CSAM(Communications for Statistical Applications and Methods) 29권 5호 발행 연도 : 2022 페이지 : pp. 615-627 (13 pages)

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Principal Fitted Component (PFC) is a semi-parametric sufficient dimension reduction (SDR) method, which is originally proposed in Cook (2007). According to Cook (2007), the PFC has a connection with other usual non-parametric SDR methods. The connection is limited to sliced inverse regression (Li, 1991) and ordinary least squares. Since there is no direct comparison between the two approaches in various forward regressions up to date, a practical guidance between the two approaches is necessary for usual statistical practitioners. To fill this practical necessity, in this paper, we newly derive a connection of the PFC to covariance methods (Yin and Cook, 2002), which is one of the most popular SDR methods. Also, intensive numerical studies have done closely to examine and compare the estimation performances of the semi- and non-parametric SDR methods for various forward regressions. The founding from the numerical studies are confirmed in a real data example.

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