Andrea Aparicio Castro, Arkadiusz Wiśniowski, Francisco Rowe
Abstract Censuses are an important source of international migration flow data. However, their use is limited since they indirectly reflect migration, capturing migrant transitions over long intervals rather than migration events, whilst also underestimating the number of infants and deaths. Censuses also neglect migration of those who are native-born when they only include questions on country of birth, and have sparse temporal availability. We propose a Bayesian hierarchical model to overcome these limitations and produce a set of robust annual migration flow estimates for South American countries. Our model translates five-year transition data from censuses into annual series, corrects biases that arise due to differences in measurement and census data quality across countries, and is grounded in migration theory to impute missing migration data between censuses.
{"title":"A Bayesian approach to estimate annual bilateral migration flows for South America using census data","authors":"Andrea Aparicio Castro, Arkadiusz Wiśniowski, Francisco Rowe","doi":"10.1093/jrsssa/qnad127","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad127","url":null,"abstract":"Abstract Censuses are an important source of international migration flow data. However, their use is limited since they indirectly reflect migration, capturing migrant transitions over long intervals rather than migration events, whilst also underestimating the number of infants and deaths. Censuses also neglect migration of those who are native-born when they only include questions on country of birth, and have sparse temporal availability. We propose a Bayesian hierarchical model to overcome these limitations and produce a set of robust annual migration flow estimates for South American countries. Our model translates five-year transition data from censuses into annual series, corrects biases that arise due to differences in measurement and census data quality across countries, and is grounded in migration theory to impute missing migration data between censuses.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"4 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136229038","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}
Journal Article Exploring Modeling with Data and Differential Equations Using R Get access Exploring Modeling with Data and Differential Equations Using R, by John Zobitz, 2022, Chapman & Hall/CRC Press, Boca Raton, FL, 356 pages, £74.99 (Hardcover), ISBN: 978-1032259482 Stanley E Lazic Stanley E Lazic Prioris.ai Inc., Canada stan.lazic@cantab.net https://orcid.org/0000-0002-3409-3407 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad133, https://doi.org/10.1093/jrsssa/qnad133 Published: 14 November 2023
{"title":"Exploring Modeling with Data and Differential Equations Using R","authors":"Stanley E Lazic","doi":"10.1093/jrsssa/qnad133","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad133","url":null,"abstract":"Journal Article Exploring Modeling with Data and Differential Equations Using R Get access Exploring Modeling with Data and Differential Equations Using R, by John Zobitz, 2022, Chapman & Hall/CRC Press, Boca Raton, FL, 356 pages, £74.99 (Hardcover), ISBN: 978-1032259482 Stanley E Lazic Stanley E Lazic Prioris.ai Inc., Canada stan.lazic@cantab.net https://orcid.org/0000-0002-3409-3407 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad133, https://doi.org/10.1093/jrsssa/qnad133 Published: 14 November 2023","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"106 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134956910","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}
Lingxiao Wang, Yan Li, Barry I Graubard, Hormuzd A Katki
Abstract Representative risk estimation is fundamental to clinical decision-making. However, risks are often estimated from non-representative epidemiologic studies, which usually under-represent minorities. Model-based methods use population registries to improve external validity of risk estimation but assume hazard ratios are generalisable from samples to the target finite population. ‘Pseudoweighting’ methods improve representativeness of studies by using an external probability-based survey as the reference, but the resulting estimators can be biased due to propensity model misspecification and inefficient due to highly variable pseudoweights or small sample sizes of minorities in the cohort and/or survey. We propose a two-step pseudoweighting procedure that post-stratifies the event rates among age/race/sex strata in the pseudoweighted cohort to the population rates, to produce efficient and robust pure risk estimation (i.e. a cause-specific absolute risk in the absence of competing events). For developing an all-cause mortality risk model representative for the USA, our findings suggest that hazard ratios for minorities are not generalisable, and that surveys can have inadequate numbers of events for minorities. Post-stratification on event rates is crucial for obtaining reliable risk estimation for minority subgroups.
{"title":"Representative pure risk estimation by using data from epidemiologic studies, surveys, and registries: estimating risks for minority subgroups","authors":"Lingxiao Wang, Yan Li, Barry I Graubard, Hormuzd A Katki","doi":"10.1093/jrsssa/qnad124","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad124","url":null,"abstract":"Abstract Representative risk estimation is fundamental to clinical decision-making. However, risks are often estimated from non-representative epidemiologic studies, which usually under-represent minorities. Model-based methods use population registries to improve external validity of risk estimation but assume hazard ratios are generalisable from samples to the target finite population. ‘Pseudoweighting’ methods improve representativeness of studies by using an external probability-based survey as the reference, but the resulting estimators can be biased due to propensity model misspecification and inefficient due to highly variable pseudoweights or small sample sizes of minorities in the cohort and/or survey. We propose a two-step pseudoweighting procedure that post-stratifies the event rates among age/race/sex strata in the pseudoweighted cohort to the population rates, to produce efficient and robust pure risk estimation (i.e. a cause-specific absolute risk in the absence of competing events). For developing an all-cause mortality risk model representative for the USA, our findings suggest that hazard ratios for minorities are not generalisable, and that surveys can have inadequate numbers of events for minorities. Post-stratification on event rates is crucial for obtaining reliable risk estimation for minority subgroups.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":" 1203","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135186451","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}
{"title":"Calyampudi Radhakrishna (CR) Rao 1920–2023","authors":"","doi":"10.1093/jrsssa/qnad131","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad131","url":null,"abstract":"","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":" 588","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135185967","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}
Abstract For policymakers wishing to make evidence-based decisions, one of the challenges is how to combine the relevant information and evidence in a coherent and defensible manner in order to formulate and evaluate candidate policies. Policymakers often need to rely on experts with disparate fields of expertise when making policy choices in complex, multi-faceted, dynamic environments such as those dealing with ecosystem services. The pressures affecting the survival and pollination capabilities of honey bees (Apis mellifera), wild bees, and other pollinators is well documented, but incomplete. In order to estimate the potential effectiveness of various candidate policies to support pollination services, there is an urgent need to quantify the effect of various combinations of variables on the pollination ecosystem service, utilising available information, models and expert judgement. In this paper, we present a new application of the integrating decision support system methodology, using dynamic Bayesian networks, for combining inputs from multiple panels of experts to evaluate policies to support an abundant pollinator population.
{"title":"Where the bee sucks: a dynamic Bayesian network approach to decision support for pollinator abundance strategies","authors":"Martine J Barons, Aditi Shenvi","doi":"10.1093/jrsssa/qnad126","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad126","url":null,"abstract":"Abstract For policymakers wishing to make evidence-based decisions, one of the challenges is how to combine the relevant information and evidence in a coherent and defensible manner in order to formulate and evaluate candidate policies. Policymakers often need to rely on experts with disparate fields of expertise when making policy choices in complex, multi-faceted, dynamic environments such as those dealing with ecosystem services. The pressures affecting the survival and pollination capabilities of honey bees (Apis mellifera), wild bees, and other pollinators is well documented, but incomplete. In order to estimate the potential effectiveness of various candidate policies to support pollination services, there is an urgent need to quantify the effect of various combinations of variables on the pollination ecosystem service, utilising available information, models and expert judgement. In this paper, we present a new application of the integrating decision support system methodology, using dynamic Bayesian networks, for combining inputs from multiple panels of experts to evaluate policies to support an abundant pollinator population.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"54 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976143","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}
Abstract Recently, there has been great interest in estimating the conditional average treatment effect using flexible machine learning methods. However, in practice, investigators often have working hypotheses about effect heterogeneity across pre-defined subgroups of study units, which we call the groupwise approach. The paper compares two modern ways to estimate groupwise treatment effects, a non-parametric approach and a semi-parametric approach, with the goal of better informing practice. Specifically, we compare (a) the underlying assumptions, (b) efficiency and adaption to the underlying data generating models, and (c) a way to combine the two approaches. We also discuss how to test a key assumption concerning the semi-parametric estimator and to obtain cluster-robust standard errors if study units in the same subgroups are correlated. We demonstrate our findings by conducting simulation studies and reanalysing the Early Childhood Longitudinal Study.
{"title":"A groupwise approach for inferring heterogeneous treatment effects in causal inference","authors":"Chan Park, Hyunseung Kang","doi":"10.1093/jrsssa/qnad125","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad125","url":null,"abstract":"Abstract Recently, there has been great interest in estimating the conditional average treatment effect using flexible machine learning methods. However, in practice, investigators often have working hypotheses about effect heterogeneity across pre-defined subgroups of study units, which we call the groupwise approach. The paper compares two modern ways to estimate groupwise treatment effects, a non-parametric approach and a semi-parametric approach, with the goal of better informing practice. Specifically, we compare (a) the underlying assumptions, (b) efficiency and adaption to the underlying data generating models, and (c) a way to combine the two approaches. We also discuss how to test a key assumption concerning the semi-parametric estimator and to obtain cluster-robust standard errors if study units in the same subgroups are correlated. We demonstrate our findings by conducting simulation studies and reanalysing the Early Childhood Longitudinal Study.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"69 3-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455542","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}
{"title":"Sanmitra Ghosh’s contribution to the Discussion of “The Second Discussion Meeting on Statistical aspects of the Covid-19 Pandemic”","authors":"Sanmita Ghosh","doi":"10.1093/jrsssa/qnad048","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad048","url":null,"abstract":"","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135617571","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}
Journal Article Introduction to Statistics and Data Analysis (with Exercises, Solutions and Applications in R) Get access Introduction to Statistics and Data Analysis (with Exercises, Solutions and Applications in R) by Christian Heumann, Michael Schomaker and Shalabh, Springer, 2023. 2nd Edition, 584pp. ISBN: 978-3-031-11832-6 (hardback), 978-3-031-11833-3 (e-book), EUR 84.99 (hardback), EUR 71.68 (e-book) Anoop Chaturvedi Anoop Chaturvedi Department of Statistics, University of Allahabad, Prayagraj, 211004, India anoopchaturv@gmail.com https://orcid.org/0000-0002-7322-3331 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad123, https://doi.org/10.1093/jrsssa/qnad123 Published: 18 October 2023
获取Christian Heumann, Michael Schomaker和Shalabh的《统计和数据分析概论》(with Exercises, Solutions and Applications in R)， Springer, 2023。第二版，584页。ISBN: 978-3-031-11832-6(精装本)，978-3-031-11833-3(电子书)，欧元84.99(精装本)，欧元71.68(电子书)Anoop Chaturvedi Anoop Chaturvedi统计系，阿拉哈巴德大学，Prayagraj, 211004，印度anoopchaturv@gmail.com https://orcid.org/0000-0002-7322-3331搜索作者的其他作品:牛津学术谷歌皇家统计学会学者杂志系列A:社会统计，qnad123, https://doi.org/10.1093/jrsssa/qnad123出版:2023年10月18日
{"title":"Introduction to Statistics and Data Analysis (with Exercises, Solutions and Applications in R)","authors":"Anoop Chaturvedi","doi":"10.1093/jrsssa/qnad123","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad123","url":null,"abstract":"Journal Article Introduction to Statistics and Data Analysis (with Exercises, Solutions and Applications in R) Get access Introduction to Statistics and Data Analysis (with Exercises, Solutions and Applications in R) by Christian Heumann, Michael Schomaker and Shalabh, Springer, 2023. 2nd Edition, 584pp. ISBN: 978-3-031-11832-6 (hardback), 978-3-031-11833-3 (e-book), EUR 84.99 (hardback), EUR 71.68 (e-book) Anoop Chaturvedi Anoop Chaturvedi Department of Statistics, University of Allahabad, Prayagraj, 211004, India anoopchaturv@gmail.com https://orcid.org/0000-0002-7322-3331 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad123, https://doi.org/10.1093/jrsssa/qnad123 Published: 18 October 2023","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135824385","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}
Journal Article Perspectives in Sustainable Equity Investing Get access Perspectives in Sustainable Equity Investing edited by Guillaume Coqueret, 1st edition, CRC Press. 2022. ix + 193 pp. ISBN 9781032071015. £49.99 (hardback) & £17.09 (eBook) Suryakumar Murugaiah Suryakumar Murugaiah Department of Management Studies, Periyar University, Salem, India suryakumarmprims@periyaruniversity.ac.in https://orcid.org/0000-0003-3872-3776 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad121, https://doi.org/10.1093/jrsssa/qnad121 Published: 26 September 2023
{"title":"Perspectives in Sustainable Equity Investing","authors":"Suryakumar Murugaiah","doi":"10.1093/jrsssa/qnad121","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad121","url":null,"abstract":"Journal Article Perspectives in Sustainable Equity Investing Get access Perspectives in Sustainable Equity Investing edited by Guillaume Coqueret, 1st edition, CRC Press. 2022. ix + 193 pp. ISBN 9781032071015. £49.99 (hardback) & £17.09 (eBook) Suryakumar Murugaiah Suryakumar Murugaiah Department of Management Studies, Periyar University, Salem, India suryakumarmprims@periyaruniversity.ac.in https://orcid.org/0000-0003-3872-3776 Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad121, https://doi.org/10.1093/jrsssa/qnad121 Published: 26 September 2023","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135719009","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}
Journal Article Accepted manuscript Authors’ reply to the Discussion of “A system of population estimates compiled from administrative data only” by John Dunne and Li-Chun Zhang Get access John Dunne, John Dunne Central Statistics Office (CSO), Cork, Ireland E-mail: John.Dunne@cso.ie Search for other works by this author on: Oxford Academic Google Scholar Li-chun Zhang Li-chun Zhang University of Southampton, Southampton, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad120, https://doi.org/10.1093/jrsssa/qnad120 Published: 14 September 2023 Article history Received: 28 August 2023 Accepted: 31 August 2023 Published: 14 September 2023
{"title":"Authors’ reply to the Discussion of “A system of population estimates compiled from administrative data only” by John Dunne and Li-Chun Zhang","authors":"John Dunne, Li-chun Zhang","doi":"10.1093/jrsssa/qnad120","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad120","url":null,"abstract":"Journal Article Accepted manuscript Authors’ reply to the Discussion of “A system of population estimates compiled from administrative data only” by John Dunne and Li-Chun Zhang Get access John Dunne, John Dunne Central Statistics Office (CSO), Cork, Ireland E-mail: John.Dunne@cso.ie Search for other works by this author on: Oxford Academic Google Scholar Li-chun Zhang Li-chun Zhang University of Southampton, Southampton, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad120, https://doi.org/10.1093/jrsssa/qnad120 Published: 14 September 2023 Article history Received: 28 August 2023 Accepted: 31 August 2023 Published: 14 September 2023","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135552837","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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