Pub Date : 2023-03-31DOI: 10.29220/csam.2023.30.2.215
Yejin Hwang, Jongwoo Song
{"title":"Recent deep learning methods for tabular data","authors":"Yejin Hwang, Jongwoo Song","doi":"10.29220/csam.2023.30.2.215","DOIUrl":"https://doi.org/10.29220/csam.2023.30.2.215","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43950579","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 : 2023-03-31DOI: 10.29220/csam.2023.30.2.119
K. Kim, MiRu Ma, Kyeong-Oh Lee
{"title":"Prediction of spatio-temporal AQI data","authors":"K. Kim, MiRu Ma, Kyeong-Oh Lee","doi":"10.29220/csam.2023.30.2.119","DOIUrl":"https://doi.org/10.29220/csam.2023.30.2.119","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45937951","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 : 2023-03-31DOI: 10.29220/csam.2023.30.2.191
A. Hassan, Reem S Alharbi
{"title":"Different estimation methods for the unit inverse exponentiated weibull distribution","authors":"A. Hassan, Reem S Alharbi","doi":"10.29220/csam.2023.30.2.191","DOIUrl":"https://doi.org/10.29220/csam.2023.30.2.191","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"1 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69824368","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 : 2023-03-31DOI: 10.29220/csam.2023.30.2.135
Hao Lia, Seokho Lee
{"title":"Least clipped absolute deviation for robust regression using skipped median","authors":"Hao Lia, Seokho Lee","doi":"10.29220/csam.2023.30.2.135","DOIUrl":"https://doi.org/10.29220/csam.2023.30.2.135","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44978654","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 : 2023-01-31DOI: 10.29220/csam.2023.30.1.021
M. Shin, Jea-Young Lee
{"title":"Nomogram for screening the risk of developing metabolic syndrome using naïve Bayesian classifier","authors":"M. Shin, Jea-Young Lee","doi":"10.29220/csam.2023.30.1.021","DOIUrl":"https://doi.org/10.29220/csam.2023.30.1.021","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44092455","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 : 2023-01-31DOI: 10.29220/csam.2023.30.1.065
Kyungeun Kim, Shin-Jae Lee, S. Eo, HyungJun Cho, Jae Won Lee
{"title":"Modified partial least squares method implementing mixed-effect model","authors":"Kyungeun Kim, Shin-Jae Lee, S. Eo, HyungJun Cho, Jae Won Lee","doi":"10.29220/csam.2023.30.1.065","DOIUrl":"https://doi.org/10.29220/csam.2023.30.1.065","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41950711","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 : 2023-01-31DOI: 10.29220/csam.2023.30.1.095
Idris Demirsoy, F. Huffer
Purpose: The statistical analysis of point processes on linear networks is a recent area of research that studies processes of events happening randomly in space (or space-time) but with locations limited to reside on a linear network. For example, tra ffi c accidents happen at random places that are limited to lying on a network of streets. This paper applies techniques developed for point processes on linear networks and the tools available in the R-package spatstat to estimate the intensity of tra ffi c accidents in Leon County, Florida. Methods: The intensity of accidents on the linear network of streets is estimated using log-linear Poisson models which incorporate cubic basis spline ( B -spline) terms which are functions of the x and y coordinates. The splines used equally-spaced knots. Ten di ff erent models are fit to the data using a variety of covariates. The models are compared with each other using an analysis of deviance for nested models. Results: We found all covariates contributed significantly to the model. AIC and BIC were used to select 9 as the number of knots. Additionally, covariates have di ff erent e ff ects such as increasing the speed limit would decrease tra ffi c accident intensity by 0.9794 but increasing the number of lanes would result in an increase in the intensity of tra ffi c accidents by 1.086. Conclusion: Our analysis shows that if other conditions are held fixed, the number of accidents actually decreases on roads with higher speed limits. The software we currently use allows our models to contain only spatial covariates and does not permit the use of temporal or space-time covariates. We would like to extend our models to include such covariates which would allow us to include weather conditions or the presence of special events (football games or concerts) as covariates.
{"title":"Intensity estimation with log-linear Poisson model on linear networks","authors":"Idris Demirsoy, F. Huffer","doi":"10.29220/csam.2023.30.1.095","DOIUrl":"https://doi.org/10.29220/csam.2023.30.1.095","url":null,"abstract":"Purpose: The statistical analysis of point processes on linear networks is a recent area of research that studies processes of events happening randomly in space (or space-time) but with locations limited to reside on a linear network. For example, tra ffi c accidents happen at random places that are limited to lying on a network of streets. This paper applies techniques developed for point processes on linear networks and the tools available in the R-package spatstat to estimate the intensity of tra ffi c accidents in Leon County, Florida. Methods: The intensity of accidents on the linear network of streets is estimated using log-linear Poisson models which incorporate cubic basis spline ( B -spline) terms which are functions of the x and y coordinates. The splines used equally-spaced knots. Ten di ff erent models are fit to the data using a variety of covariates. The models are compared with each other using an analysis of deviance for nested models. Results: We found all covariates contributed significantly to the model. AIC and BIC were used to select 9 as the number of knots. Additionally, covariates have di ff erent e ff ects such as increasing the speed limit would decrease tra ffi c accident intensity by 0.9794 but increasing the number of lanes would result in an increase in the intensity of tra ffi c accidents by 1.086. Conclusion: Our analysis shows that if other conditions are held fixed, the number of accidents actually decreases on roads with higher speed limits. The software we currently use allows our models to contain only spatial covariates and does not permit the use of temporal or space-time covariates. We would like to extend our models to include such covariates which would allow us to include weather conditions or the presence of special events (football games or concerts) as covariates.","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43565051","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 : 2023-01-31DOI: 10.29220/csam.2023.30.1.109
Sang Kyu Lee, Hyoung-Moon Kim
{"title":"Two tests using more assumptions but lower power","authors":"Sang Kyu Lee, Hyoung-Moon Kim","doi":"10.29220/csam.2023.30.1.109","DOIUrl":"https://doi.org/10.29220/csam.2023.30.1.109","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":"1 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69824305","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 : 2023-01-31DOI: 10.29220/csam.2023.30.1.037
S. Shin, Jun Hong Kim, Yong-Seok Choi
{"title":"Estimation of missing landmarks in statistical shape analysis","authors":"S. Shin, Jun Hong Kim, Yong-Seok Choi","doi":"10.29220/csam.2023.30.1.037","DOIUrl":"https://doi.org/10.29220/csam.2023.30.1.037","url":null,"abstract":"","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45650178","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 : 2023-01-31DOI: 10.29220/csam.2023.30.1.075
Sanghee Kim, Seongjoo Song
The growing trend of cyber risk has put forward the importance of cyber risk management. Cyber risk is defined as an accidental or intentional risk related to information and technology assets. Although cyber risk is a subset of operational risk, it is reported to be handled di ff erently from operational risk due to its di ff erent features of the loss distribution. In this study, we aim to detect the characteristics of cyber loss and find a suitable model by measuring value at risk (VaR). We use the loss distribution approach (LDA) and the time series model to describe cyber losses of financial and non-financial business sectors, provided in SAS R (cid:79) OpRisk Global Data. Peaks over threshold (POT) method is also incorporated to improve the risk measurement. For the financial sector, the LDA and GARCH model with POT perform better than those without POT, respectively. The same result is obtained for the non-financial sector, although the di ff erences are not significant. We also build a two-dimensional model reflecting the dependence structure between financial and non-financial sectors through a bivariate copula and check the model adequacy through VaR.
网络风险的增长趋势提出了网络风险管理的重要性。网络风险被定义为与信息技术资产相关的意外或故意风险。虽然网络风险是操作风险的一个子集，但由于其损失分布的不同特征，其处理方法与操作风险不同。在本研究中，我们旨在通过测量风险值(VaR)来检测网络损失的特征，并找到合适的模型。我们使用损失分布方法(LDA)和时间序列模型来描述SAS R (cid:79) OpRisk Global Data提供的金融和非金融业务部门的网络损失。引入了阈值以上峰值(POT)方法来改进风险度量。对于金融部门，有POT的LDA和GARCH模型分别比没有POT的表现更好。非金融部门也得到了同样的结果，尽管差异并不显著。通过二元联结建立了反映金融部门与非金融部门依赖结构的二维模型，并通过VaR检验了模型的充分性。
{"title":"Cyber risk measurement via loss distribution approach and GARCH model","authors":"Sanghee Kim, Seongjoo Song","doi":"10.29220/csam.2023.30.1.075","DOIUrl":"https://doi.org/10.29220/csam.2023.30.1.075","url":null,"abstract":"The growing trend of cyber risk has put forward the importance of cyber risk management. Cyber risk is defined as an accidental or intentional risk related to information and technology assets. Although cyber risk is a subset of operational risk, it is reported to be handled di ff erently from operational risk due to its di ff erent features of the loss distribution. In this study, we aim to detect the characteristics of cyber loss and find a suitable model by measuring value at risk (VaR). We use the loss distribution approach (LDA) and the time series model to describe cyber losses of financial and non-financial business sectors, provided in SAS R (cid:79) OpRisk Global Data. Peaks over threshold (POT) method is also incorporated to improve the risk measurement. For the financial sector, the LDA and GARCH model with POT perform better than those without POT, respectively. The same result is obtained for the non-financial sector, although the di ff erences are not significant. We also build a two-dimensional model reflecting the dependence structure between financial and non-financial sectors through a bivariate copula and check the model adequacy through VaR.","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48689643","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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