Communications for Statistical Applications and Methods最新文献_第6页

A pooled Bayes test of independence using restricted pooling model for contingency tables from small areas 用限制池化模型对小区域列联表进行了池化贝叶斯独立性检验

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Communications for Statistical Applications and Methods Pub Date : 2022-09-30 DOI: 10.29220/csam.2022.29.5.547

A. Jo, D. Kim

{"title":"A pooled Bayes test of independence using restricted pooling model for contingency tables from small areas","authors":"A. Jo, D. Kim","doi":"10.29220/csam.2022.29.5.547","DOIUrl":"https://doi.org/10.29220/csam.2022.29.5.547","url":null,"abstract":"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 di ﬃ cult 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.","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44235913","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

Naive Bayes classifiers boosted by sufficient dimension reduction: applications to top-k classification 充分降维的朴素贝叶斯分类器:top-k分类的应用

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Communications for Statistical Applications and Methods Pub Date : 2022-09-30 DOI: 10.29220/csam.2022.29.5.603

Su Hyeong Yang, S. Shin, Woo-Chang Sung, Choon Won Lee

引用次数: 0

Comparison of covariance thresholding methods in gene set analysis 基因集分析中协方差阈值法的比较

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Communications for Statistical Applications and Methods Pub Date : 2022-09-30 DOI: 10.29220/csam.2022.29.5.591

Sora Park, Kipoong Kim, Hokeun Sun

引用次数: 0

Intensive comparison of semi-parametric and non-parametric dimension reduction methods in forward regression 前向回归中半参数降维和非参数降维方法的深入比较

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Communications for Statistical Applications and Methods Pub Date : 2022-09-30 DOI: 10.29220/csam.2022.29.5.615

Minju Shin, J. Yoo

引用次数: 0

Real-time prediction for multi-wave COVID-19 outbreaks 多波新冠肺炎疫情的实时预测

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Communications for Statistical Applications and Methods Pub Date : 2022-09-30 DOI: 10.29220/csam.2022.29.5.499

F. Zuhairoh, D. Rosadi

{"title":"Real-time prediction for multi-wave COVID-19 outbreaks","authors":"F. Zuhairoh, D. Rosadi","doi":"10.29220/csam.2022.29.5.499","DOIUrl":"https://doi.org/10.29220/csam.2022.29.5.499","url":null,"abstract":"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.","PeriodicalId":44931,"journal":{"name":"Communications for Statistical Applications and Methods","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48268326","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: 1

Comparison of tree-based ensemble models for regression 基于树的集成回归模型的比较

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Communications for Statistical Applications and Methods Pub Date : 2022-09-30 DOI: 10.29220/csam.2022.29.5.561

Sangho Park, C. Kim

引用次数: 2

Monitoring social networks based on transformation into categorical data 基于分类数据转换的社交网络监控

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Communications for Statistical Applications and Methods Pub Date : 2022-07-31 DOI: 10.29220/csam.2022.29.4.487

J. Lee, Jaeheon Lee

引用次数: 0

A Kullback-Leibler divergence based comparison of approximate Bayesian estimations of ARMA models 基于Kullback-Leibler散度的ARMA模型近似贝叶斯估计的比较

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Communications for Statistical Applications and Methods Pub Date : 2022-07-31 DOI: 10.29220/csam.2022.29.4.471

A. Amin

引用次数: 1

ADMM for least square problems with pairwise-difference penalties for coefficient grouping 具有系数分组两两差分惩罚的最小二乘问题的ADMM

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Communications for Statistical Applications and Methods Pub Date : 2022-07-31 DOI: 10.29220/csam.2022.29.4.441

So-Hyun Parka, S. Shin

引用次数: 1

An R package UnifiedDoseFinding for continuous and ordinal outcomes in Phase I dose-finding trials R包UnifiedDoseFinding用于I期剂量发现试验的连续和有序结果

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Communications for Statistical Applications and Methods Pub Date : 2022-07-31 DOI: 10.29220/csam.2022.29.4.421

H. Pan, Rongji Mu, Chia-Wei Hsu, Shouhao Zhou

引用次数: 0