Communications for Statistical Applications and Methods最新文献_第4页

Recent deep learning methods for tabular data 最近的表格数据深度学习方法

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Communications for Statistical Applications and Methods Pub Date : 2023-03-31 DOI: 10.29220/csam.2023.30.2.215

Yejin Hwang, Jongwoo Song

引用次数: 1

Prediction of spatio-temporal AQI data 时空AQI数据预测

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Communications for Statistical Applications and Methods Pub Date : 2023-03-31 DOI: 10.29220/csam.2023.30.2.119

K. Kim, MiRu Ma, Kyeong-Oh Lee

引用次数: 0

Different estimation methods for the unit inverse exponentiated weibull distribution 单位逆指数威布尔分布的不同估计方法

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Communications for Statistical Applications and Methods Pub Date : 2023-03-31 DOI: 10.29220/csam.2023.30.2.191

A. Hassan, Reem S Alharbi

引用次数: 2

Least clipped absolute deviation for robust regression using skipped median 使用跳过中位数的稳健回归的最小剪切绝对偏差

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Communications for Statistical Applications and Methods Pub Date : 2023-03-31 DOI: 10.29220/csam.2023.30.2.135

Hao Lia, Seokho Lee

引用次数: 0

Nomogram for screening the risk of developing metabolic syndrome using naïve Bayesian classifier 利用naïve贝叶斯分类器筛选代谢综合征发生风险的Nomogram

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Communications for Statistical Applications and Methods Pub Date : 2023-01-31 DOI: 10.29220/csam.2023.30.1.021

M. Shin, Jea-Young Lee

引用次数: 0

Modified partial least squares method implementing mixed-effect model 实现混合效应模型的改进偏最小二乘法

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Communications for Statistical Applications and Methods Pub Date : 2023-01-31 DOI: 10.29220/csam.2023.30.1.065

Kyungeun Kim, Shin-Jae Lee, S. Eo, HyungJun Cho, Jae Won Lee

引用次数: 0

Intensity estimation with log-linear Poisson model on linear networks 线性网络上对数线性泊松模型的强度估计

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Communications for Statistical Applications and Methods Pub Date : 2023-01-31 DOI: 10.29220/csam.2023.30.1.095

Idris Demirsoy, F. Huffer

{"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}

引用次数: 0

Two tests using more assumptions but lower power 两个测试使用更多的假设，但更低的功率

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Communications for Statistical Applications and Methods Pub Date : 2023-01-31 DOI: 10.29220/csam.2023.30.1.109

Sang Kyu Lee, Hyoung-Moon Kim

引用次数: 1

Estimation of missing landmarks in statistical shape analysis 统计形状分析中缺失标志的估计

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Communications for Statistical Applications and Methods Pub Date : 2023-01-31 DOI: 10.29220/csam.2023.30.1.037

S. Shin, Jun Hong Kim, Yong-Seok Choi

引用次数: 1

Cyber risk measurement via loss distribution approach and GARCH model 基于损失分布方法和GARCH模型的网络风险度量

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Communications for Statistical Applications and Methods Pub Date : 2023-01-31 DOI: 10.29220/csam.2023.30.1.075

Sanghee Kim, Seongjoo Song

{"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}

引用次数: 0