Pub Date : 2022-02-16DOI: 10.1007/s42081-022-00146-2
Victor Mooto Nawa
{"title":"A weighted score confidence interval for a binomial proportion","authors":"Victor Mooto Nawa","doi":"10.1007/s42081-022-00146-2","DOIUrl":"https://doi.org/10.1007/s42081-022-00146-2","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"5 1","pages":"133 - 147"},"PeriodicalIF":1.3,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47620088","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 : 2022-01-31DOI: 10.1007/s42081-022-00145-3
Yasuhito Tsuruta, Masahiko Sagae
{"title":"Improving kernel-based nonparametric regression for circular–linear data","authors":"Yasuhito Tsuruta, Masahiko Sagae","doi":"10.1007/s42081-022-00145-3","DOIUrl":"https://doi.org/10.1007/s42081-022-00145-3","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"5 1","pages":"111 - 131"},"PeriodicalIF":1.3,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53297295","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 : 2022-01-20DOI: 10.1007/s42081-023-00213-2
Yuko Kakikawa, Kaito Shimamura, Shuichi Kawano
{"title":"Bayesian fused lasso modeling via horseshoe prior","authors":"Yuko Kakikawa, Kaito Shimamura, Shuichi Kawano","doi":"10.1007/s42081-023-00213-2","DOIUrl":"https://doi.org/10.1007/s42081-023-00213-2","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"1 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42997566","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 : 2022-01-18DOI: 10.1007/s42081-022-00155-1
Mitsuki Kobayashi, Y. Shimizu
{"title":"Least-squares estimators based on the Adams method for stochastic differential equations with small Lévy noise","authors":"Mitsuki Kobayashi, Y. Shimizu","doi":"10.1007/s42081-022-00155-1","DOIUrl":"https://doi.org/10.1007/s42081-022-00155-1","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"5 1","pages":"217 - 240"},"PeriodicalIF":1.3,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48900408","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 : 2022-01-14DOI: 10.1007/s42081-021-00142-y
H. Rehman, N. Chandra
{"title":"Inferences on cumulative incidence function for middle censored survival data with Weibull regression","authors":"H. Rehman, N. Chandra","doi":"10.1007/s42081-021-00142-y","DOIUrl":"https://doi.org/10.1007/s42081-021-00142-y","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"5 1","pages":"65 - 86"},"PeriodicalIF":1.3,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53297203","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 : 2022-01-06DOI: 10.1007/s42081-021-00143-x
Anqi Li, Takaki Sato, Y. Matsuda
{"title":"Spatial analysis of subjective well-being in Japan","authors":"Anqi Li, Takaki Sato, Y. Matsuda","doi":"10.1007/s42081-021-00143-x","DOIUrl":"https://doi.org/10.1007/s42081-021-00143-x","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"5 1","pages":"87 - 110"},"PeriodicalIF":1.3,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49371693","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 : 2022-01-01Epub Date: 2022-06-28DOI: 10.1007/s42081-022-00165-z
Ahmad R Alsaber, Parul Setiya, Ahmad T Al-Sultan, Jiazhu Pan
In urban areas, air pollution is one of the most serious global environmental issues. Using time-series approaches, this study looked into the validity of the relationship between air pollution and COVID-19 hospitalization. This time series research was carried out in the state of Kuwait; stationarity test, cointegration test, Granger causality and stability test, and test on multivariate time-series using the Vector Error Correction Model (VECM) technique. The findings reveal that the concentration rate of air pollutants ( , , , , and ) has an effect on COVID-19 admitted cases via Granger-cause. The Granger causation test shows that the concentration rate of air pollutants ( , , , temperature and wind speed) influences and predicts the COVID-19 admitted cases. The findings suggest that sulfur dioxide ( ), , temperature, and wind speed induce an increase in COVID-19 admitted cases in the short term according to VECM analysis. The evidence of a positive long-run association between COVID-19 admitted cases and environmental air pollution might be shown in the cointegration test and the VECM. There is an affirmation that the usage of air pollutants ( , , , , and ) has a significant impact on COVID-19-admitted cases' prediction and its explained about 24% of increasing COVID-19 admitted cases in Kuwait.
{"title":"Exploring the impact of air pollution on COVID-19 admitted cases: Evidence from vector error correction model (VECM) approach in explaining the relationship between air pollutants towards COVID-19 cases in Kuwait.","authors":"Ahmad R Alsaber, Parul Setiya, Ahmad T Al-Sultan, Jiazhu Pan","doi":"10.1007/s42081-022-00165-z","DOIUrl":"https://doi.org/10.1007/s42081-022-00165-z","url":null,"abstract":"<p><p>In urban areas, air pollution is one of the most serious global environmental issues. Using time-series approaches, this study looked into the validity of the relationship between air pollution and COVID-19 hospitalization. This time series research was carried out in the state of Kuwait; stationarity test, cointegration test, Granger causality and stability test, and test on multivariate time-series using the Vector Error Correction Model (VECM) technique. The findings reveal that the concentration rate of air pollutants ( <math><msub><mtext>O</mtext> <mn>3</mn></msub> </math> , <math><msub><mtext>SO</mtext> <mn>2</mn></msub> </math> , <math><msub><mtext>NO</mtext> <mn>2</mn></msub> </math> , <math><mtext>CO</mtext></math> , and <math><msub><mtext>PM</mtext> <mn>10</mn></msub> </math> ) has an effect on COVID-19 admitted cases via Granger-cause. The Granger causation test shows that the concentration rate of air pollutants ( <math><msub><mtext>O</mtext> <mn>3</mn></msub> </math> , <math><msub><mtext>PM</mtext> <mn>10</mn></msub> </math> , <math><msub><mtext>NO</mtext> <mn>2</mn></msub> </math> , temperature and wind speed) influences and predicts the COVID-19 admitted cases. The findings suggest that sulfur dioxide ( <math><msub><mtext>SO</mtext> <mn>2</mn></msub> </math> ), <math><msub><mtext>NO</mtext> <mn>2</mn></msub> </math> , temperature, and wind speed induce an increase in COVID-19 admitted cases in the short term according to VECM analysis. The evidence of a positive long-run association between COVID-19 admitted cases and environmental air pollution might be shown in the cointegration test and the VECM. There is an affirmation that the usage of air pollutants ( <math><msub><mtext>O</mtext> <mn>3</mn></msub> </math> , <math><msub><mtext>SO</mtext> <mn>2</mn></msub> </math> , <math><msub><mtext>NO</mtext> <mn>2</mn></msub> </math> , <math><mtext>CO</mtext></math> , and <math><msub><mtext>PM</mtext> <mn>10</mn></msub> </math> ) has a significant impact on COVID-19-admitted cases' prediction and its explained about 24% of increasing COVID-19 admitted cases in Kuwait.</p>","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":" ","pages":"379-406"},"PeriodicalIF":1.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244511/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40482917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01Epub Date: 2022-03-27DOI: 10.1007/s42081-022-00151-5
Takuma Tanaka, Tetsuto Himeno, Kaoru Fueda
In 2017, Shiga University established the Faculty of Data Science, which was the first faculty in Japan specializing in data science and statistics. This paper reports the Faculty's historical context, curricula, and collaboration with industry and other universities. The career paths of the graduates and the massive open online courses and textbooks provided by the Faculty of Data Science are also summarized.
{"title":"Shiga University's endeavor to promote human resources development for data science in Japan.","authors":"Takuma Tanaka, Tetsuto Himeno, Kaoru Fueda","doi":"10.1007/s42081-022-00151-5","DOIUrl":"10.1007/s42081-022-00151-5","url":null,"abstract":"<p><p>In 2017, Shiga University established the Faculty of Data Science, which was the first faculty in Japan specializing in data science and statistics. This paper reports the Faculty's historical context, curricula, and collaboration with industry and other universities. The career paths of the graduates and the massive open online courses and textbooks provided by the Faculty of Data Science are also summarized.</p>","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"5 1","pages":"747-755"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8958343/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43496285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposes a direction for the utilization of multi-agent simulation (MAS) to consider an optimal prevention strategy for the spread of the coronavirus disease of 2019 (COVID-19) through a pandemic modeling example in Japan. MAS can flexibly express macroscopic phenomena formed through the interaction of micro-agents modeled to act autonomously. The use of MAS can provide a variety of recommendations for bringing a pandemic under control, even in the case of the COVID-19 pandemic, which has become more intense as of 2021. However, models that do not consider individual heterogeneity, such as analytical Susceptible-Exposed-Infectious-Recovered (SEIR) models, are often used as predictive models for infectious diseases and the main reference for decision-making. In this study, we show that by constructing a MAS that simulates a metropolitan city in Japan in a simple manner while considering the heterogeneity of age and other background information, we can capture the effects of various measures such as vaccinations on the spread of infections in a more realistic setting. Moreover, it is possible to offer various recommendations for optimal strategies to suppress a pandemic by combining reinforcement learning with MAS. This study explicates the potential of MAS in the development of strategies to prevent the spread of infection.
{"title":"Determination of optimal prevention strategy for COVID-19 based on multi-agent simulation.","authors":"Satoki Fujita, Ryo Kiguchi, Yuki Yoshida, Yoshitake Kitanishi","doi":"10.1007/s42081-022-00163-1","DOIUrl":"https://doi.org/10.1007/s42081-022-00163-1","url":null,"abstract":"<p><p>This study proposes a direction for the utilization of multi-agent simulation (MAS) to consider an optimal prevention strategy for the spread of the coronavirus disease of 2019 (COVID-19) through a pandemic modeling example in Japan. MAS can flexibly express macroscopic phenomena formed through the interaction of micro-agents modeled to act autonomously. The use of MAS can provide a variety of recommendations for bringing a pandemic under control, even in the case of the COVID-19 pandemic, which has become more intense as of 2021. However, models that do not consider individual heterogeneity, such as analytical Susceptible-Exposed-Infectious-Recovered (SEIR) models, are often used as predictive models for infectious diseases and the main reference for decision-making. In this study, we show that by constructing a MAS that simulates a metropolitan city in Japan in a simple manner while considering the heterogeneity of age and other background information, we can capture the effects of various measures such as vaccinations on the spread of infections in a more realistic setting. Moreover, it is possible to offer various recommendations for optimal strategies to suppress a pandemic by combining reinforcement learning with MAS. This study explicates the potential of MAS in the development of strategies to prevent the spread of infection.</p>","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":" ","pages":"339-361"},"PeriodicalIF":1.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9195403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40164743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01Epub Date: 2022-06-02DOI: 10.1007/s42081-022-00166-y
Koji Kurihara
{"title":"Special feature: statistics for COVID-19 pandemic data.","authors":"Koji Kurihara","doi":"10.1007/s42081-022-00166-y","DOIUrl":"10.1007/s42081-022-00166-y","url":null,"abstract":"","PeriodicalId":29911,"journal":{"name":"Japanese Journal of Statistics and Data Science","volume":"5 1","pages":"275-277"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9161758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48638092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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