Pub Date : 2023-11-30DOI: 10.29220/csam.2023.30.6.561
Haeji Lee, Chun Kee Chung, Jaehee Kim
Brain network analysis has attracted the interest of neuroscience researchers in studying brain diseases. Mag-netoencephalography (MEG) is especially proper for analyzing functional connectivity due to high temporal and spatial resolution. The application of graph theory for functional connectivity analysis has been studied widely, but research on network modeling for MEG still needs more. Temporal exponential random graph model (TERGM) considers temporal dependencies of networks. We performed the brain network analysis, including static / temporal network statistics, on two groups of epilepsy patients who removed the left (LT) or right (RT) part of the brain and healthy controls. We investigate network di ff erences using Multiset canonical correlation analysis (MCCA) and TERGM between epilepsy patients and healthy controls (HC). The brain network of healthy controls had fewer temporal changes than patient groups. As a result of TERGM, on the simulation networks, LT and RT had less stable state than HC in the network connectivity structure. HC had a stable state of the brain network.
脑网络分析引起了神经科学研究人员对脑部疾病研究的兴趣。脑磁图（MEG）具有很高的时间和空间分辨率，尤其适合分析功能连接性。图论在功能连通性分析中的应用已被广泛研究，但针对 MEG 的网络建模研究仍有待加强。时间指数随机图模型（TERGM）考虑了网络的时间依赖性。我们对切除左脑（LT）或右脑（RT）部分的两组癫痫患者和健康对照组进行了脑网络分析，包括静态/时间网络统计。我们使用多集典型相关分析（MCCA）和TERGM研究了癫痫患者和健康对照组（HC）之间的网络差异。与患者组相比，健康对照组大脑网络的时间变化较少。TERGM的结果是，在模拟网络中，LT和RT的网络连接结构不如HC稳定。健康对照组的大脑网络处于稳定状态。
{"title":"Statistical network analysis for epilepsy MEG data","authors":"Haeji Lee, Chun Kee Chung, Jaehee Kim","doi":"10.29220/csam.2023.30.6.561","DOIUrl":"https://doi.org/10.29220/csam.2023.30.6.561","url":null,"abstract":"Brain network analysis has attracted the interest of neuroscience researchers in studying brain diseases. Mag-netoencephalography (MEG) is especially proper for analyzing functional connectivity due to high temporal and spatial resolution. The application of graph theory for functional connectivity analysis has been studied widely, but research on network modeling for MEG still needs more. Temporal exponential random graph model (TERGM) considers temporal dependencies of networks. We performed the brain network analysis, including static / temporal network statistics, on two groups of epilepsy patients who removed the left (LT) or right (RT) part of the brain and healthy controls. We investigate network di ff erences using Multiset canonical correlation analysis (MCCA) and TERGM between epilepsy patients and healthy controls (HC). The brain network of healthy controls had fewer temporal changes than patient groups. As a result of TERGM, on the simulation networks, LT and RT had less stable state than HC in the network connectivity structure. HC had a stable state of the brain network.","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"11 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139208262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-30DOI: 10.29220/csam.2023.30.6.577
Jong-Min Kim, Kee-Jae Lee, Seung-Joo Lee
Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.
{"title":"Two-stage imputation method to handle missing data for categorical response variable","authors":"Jong-Min Kim, Kee-Jae Lee, Seung-Joo Lee","doi":"10.29220/csam.2023.30.6.577","DOIUrl":"https://doi.org/10.29220/csam.2023.30.6.577","url":null,"abstract":"Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"150 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139202273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-30DOI: 10.29220/csam.2023.30.6.589
Jieon Lim, Weonyoung Joo
Deep generative models target to infer the underlying true data distribution, and it leads to a huge success in generating fake-but-realistic data. Regarding such a perspective, the data attributes can be a crucial factor in the data generation process since non-existent counterfactual samples can be generated by altering certain factors. For example, we can generate new portrait images by flipping the gender attribute or altering the hair color attributes. This paper proposes counterfactual disentangled variational autoencoder generative adversarial networks (CDVAE-GAN), specialized for data attribute level counterfactual data generation. The structure of the proposed CDVAE-GAN consists of variational autoencoders and generative adversarial networks. Specifically, we adopt a Gaussian variational autoencoder to extract low-dimensional disentangled data features and auxiliary Bernoulli latent variables to model the data attributes separately. Also, we utilize a generative adversarial network to generate data with high fidelity. By enjoying the benefits of the variational autoencoder with the additional Bernoulli latent variables and the generative adversarial network, the proposed CDVAE-GAN can control the data attributes, and it enables producing counterfactual data. Our experimental result on the CelebA dataset qualitatively shows that the generated samples from CDVAE-GAN are realistic. Also, the quantitative results support that the proposed model can produce data that can deceive other machine learning classifiers with the altered data attributes.
{"title":"Counterfactual image generation by disentangling data attributes with deep generative models","authors":"Jieon Lim, Weonyoung Joo","doi":"10.29220/csam.2023.30.6.589","DOIUrl":"https://doi.org/10.29220/csam.2023.30.6.589","url":null,"abstract":"Deep generative models target to infer the underlying true data distribution, and it leads to a huge success in generating fake-but-realistic data. Regarding such a perspective, the data attributes can be a crucial factor in the data generation process since non-existent counterfactual samples can be generated by altering certain factors. For example, we can generate new portrait images by flipping the gender attribute or altering the hair color attributes. This paper proposes counterfactual disentangled variational autoencoder generative adversarial networks (CDVAE-GAN), specialized for data attribute level counterfactual data generation. The structure of the proposed CDVAE-GAN consists of variational autoencoders and generative adversarial networks. Specifically, we adopt a Gaussian variational autoencoder to extract low-dimensional disentangled data features and auxiliary Bernoulli latent variables to model the data attributes separately. Also, we utilize a generative adversarial network to generate data with high fidelity. By enjoying the benefits of the variational autoencoder with the additional Bernoulli latent variables and the generative adversarial network, the proposed CDVAE-GAN can control the data attributes, and it enables producing counterfactual data. Our experimental result on the CelebA dataset qualitatively shows that the generated samples from CDVAE-GAN are realistic. Also, the quantitative results support that the proposed model can produce data that can deceive other machine learning classifiers with the altered data attributes.","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"55 6","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139205791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-30DOI: 10.29220/csam.2023.30.6.631
Eujin Park, Chan Park
{"title":"Identification of indirect effects in the two-condition within-subject mediation model and its implementation using SEM","authors":"Eujin Park, Chan Park","doi":"10.29220/csam.2023.30.6.631","DOIUrl":"https://doi.org/10.29220/csam.2023.30.6.631","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"119 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139199837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-30DOI: 10.29220/csam.2023.30.6.531
R. Minkah, Tertius de Wet, Abhik Ghosh, H. Yousof
The estimation of extreme quantiles is one of the main objectives of statistics of extremes (which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The estimator is obtained through the minimum density power divergence criterion on an exponential regression model. The proposed estimator was compared with two estimators of extreme quantiles in the literature in a simulation study. The results show that the proposed estimator is stable to the choice of the number of top order statistics and show lesser bias and mean square error compared to the existing extreme quantile estimators. Practical application of the proposed estimator is illustrated with data from the pedochemical and insurance industries
{"title":"Robust extreme quantile estimation for Pareto-type tails through an exponential regression model","authors":"R. Minkah, Tertius de Wet, Abhik Ghosh, H. Yousof","doi":"10.29220/csam.2023.30.6.531","DOIUrl":"https://doi.org/10.29220/csam.2023.30.6.531","url":null,"abstract":"The estimation of extreme quantiles is one of the main objectives of statistics of extremes (which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The estimator is obtained through the minimum density power divergence criterion on an exponential regression model. The proposed estimator was compared with two estimators of extreme quantiles in the literature in a simulation study. The results show that the proposed estimator is stable to the choice of the number of top order statistics and show lesser bias and mean square error compared to the existing extreme quantile estimators. Practical application of the proposed estimator is illustrated with data from the pedochemical and insurance industries","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"23 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139200688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-30DOI: 10.29220/csam.2023.30.6.605
Marcos S Oliveira, Daniela C. R. Oliveira, V. H. Lachos
{"title":"Influence diagnostics for skew-t censored linear regression models","authors":"Marcos S Oliveira, Daniela C. R. Oliveira, V. H. Lachos","doi":"10.29220/csam.2023.30.6.605","DOIUrl":"https://doi.org/10.29220/csam.2023.30.6.605","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"1 9 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139198156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-30DOI: 10.29220/csam.2023.30.6.551
Woohyun Kim, Daeun Kim, Kyoung Shin Park, Sungim Lee
{"title":"Motion classification using distributional features of 3D skeleton data","authors":"Woohyun Kim, Daeun Kim, Kyoung Shin Park, Sungim Lee","doi":"10.29220/csam.2023.30.6.551","DOIUrl":"https://doi.org/10.29220/csam.2023.30.6.551","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"25 4","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139206301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-30DOI: 10.29220/csam.2023.30.5.517
Minjoo Jo, Rosy Oh, Man-Suk Oh
{"title":"Prediction of PM10 concentration in Seoul, Korea using Bayesian network","authors":"Minjoo Jo, Rosy Oh, Man-Suk Oh","doi":"10.29220/csam.2023.30.5.517","DOIUrl":"https://doi.org/10.29220/csam.2023.30.5.517","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136277303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-30DOI: 10.29220/csam.2023.30.5.467
Fadal Abdullah Ali Aldhufairi
{"title":"Utilizing a unit Gompertz distorted copula to model dependence in anthropometric data","authors":"Fadal Abdullah Ali Aldhufairi","doi":"10.29220/csam.2023.30.5.467","DOIUrl":"https://doi.org/10.29220/csam.2023.30.5.467","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136277299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-30DOI: 10.29220/csam.2023.30.5.485
Seungsik Kim, Nami Gu, Jeongin Moon, Keunwook Kim, Yeongeun Hwang, Kyeongjun Lee
{"title":"Comparative analysis of model performance for predicting the customer of cafeteria using unstructured data","authors":"Seungsik Kim, Nami Gu, Jeongin Moon, Keunwook Kim, Yeongeun Hwang, Kyeongjun Lee","doi":"10.29220/csam.2023.30.5.485","DOIUrl":"https://doi.org/10.29220/csam.2023.30.5.485","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136277301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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