{"title":"Bayesian projections of total fertility rate conditional on the United Nations sustainable development goals","authors":"Daphne H. Liu, A. Raftery","doi":"10.1214/23-aoas1793","DOIUrl":"https://doi.org/10.1214/23-aoas1793","url":null,"abstract":"","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"2 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140082995","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}
Chongliang Luo, Rui Duan, M. Edmondson, Jiasheng Shi, M. Maltenfort, Jeffrey S. Morris, Christopher B. Forrest, Rebecca A. Hubbard, Yong Chen
{"title":"Distributed proportional likelihood ratio model with application to data integration across clinical sites","authors":"Chongliang Luo, Rui Duan, M. Edmondson, Jiasheng Shi, M. Maltenfort, Jeffrey S. Morris, Christopher B. Forrest, Rebecca A. Hubbard, Yong Chen","doi":"10.1214/23-aoas1779","DOIUrl":"https://doi.org/10.1214/23-aoas1779","url":null,"abstract":"","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"64 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140085352","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}
Danyi Xiong, Seongoh Park, Johan Lim, Tao Wang, Xinlei Wang
{"title":"Bayesian multiple instance classification based on hierarchical probit regression","authors":"Danyi Xiong, Seongoh Park, Johan Lim, Tao Wang, Xinlei Wang","doi":"10.1214/23-aoas1780","DOIUrl":"https://doi.org/10.1214/23-aoas1780","url":null,"abstract":"","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"116 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140088044","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}
Air pollution surveillance is critically important for public health. One air pollutant, ozone, is extremely challenging to analyze properly, as it is a secondary pollutant caused by complex chemical reactions in the air, and does not emit directly into the atmosphere. Numerous environmental studies confirm that ozone concentration levels are associated with meteorological conditions, and long-term exposure to high ozone concentration levels is associated with the incidence of many diseases, including asthma, respiratory, and cardiovascular diseases. Thus, it is important to develop an air pollution surveillance system to collect both air pollution and meteorological data and monitor the data continuously over time. To this end, statistical process control (SPC) charts provide a major statistical tool. But, most existing SPC charts are designed for cases when the in-control (IC) process observations at different times are assumed to be independent and identically distributed. The air pollution and meteorological data would not satisfy these conditions due to serial data correlation, high dimensionality, seasonality, and other complex data structure. Motivated by an application to monitor the ground ozone concentration levels in the Houston-Galveston-Brazoria (HGB) area, we developed a new process monitoring method using principal component analysis and sequential learning. The new method can accommodate high dimensionality, time-varying IC process distribution, serial data correlation, and non-parametric data distribution. It is shown to be a reliable analytic tool for on-line monitoring of air quality.
空气污染监测对公众健康至关重要。有一种空气污染物--臭氧--极难进行正确分析，因为它是由空气中复杂的化学反应引起的二次污染物，不会直接排放到大气中。大量环境研究证实，臭氧浓度水平与气象条件有关，长期暴露在高浓度臭氧环境中与多种疾病的发病率有关，包括哮喘、呼吸道疾病和心血管疾病。因此，必须开发一个空气污染监测系统，收集空气污染和气象数据，并对数据进行长期连续监测。为此，统计过程控制 (SPC) 图表提供了一个重要的统计工具。但是，现有的大多数 SPC 图表都是针对假定不同时间的在控 (IC) 过程观测值是独立且同分布的情况而设计的。由于串行数据相关性、高维度、季节性和其他复杂的数据结构，空气污染和气象数据无法满足这些条件。受侯斯顿-加尔维斯顿-布拉佐里亚（HGB）地区地面臭氧浓度水平监测应用的启发，我们利用主成分分析和序列学习开发了一种新的过程监测方法。新方法可以适应高维度、时变 IC 过程分布、序列数据相关性和非参数数据分布。结果表明，它是在线监测空气质量的可靠分析工具。
{"title":"Online monitoring of air quality using PCA-based sequential learning","authors":"Xiulin Xie, Nicole Qian, Peihua Qiu","doi":"10.1214/23-aoas1803","DOIUrl":"https://doi.org/10.1214/23-aoas1803","url":null,"abstract":"Air pollution surveillance is critically important for public health. One air pollutant, ozone, is extremely challenging to analyze properly, as it is a secondary pollutant caused by complex chemical reactions in the air, and does not emit directly into the atmosphere. Numerous environmental studies confirm that ozone concentration levels are associated with meteorological conditions, and long-term exposure to high ozone concentration levels is associated with the incidence of many diseases, including asthma, respiratory, and cardiovascular diseases. Thus, it is important to develop an air pollution surveillance system to collect both air pollution and meteorological data and monitor the data continuously over time. To this end, statistical process control (SPC) charts provide a major statistical tool. But, most existing SPC charts are designed for cases when the in-control (IC) process observations at different times are assumed to be independent and identically distributed. The air pollution and meteorological data would not satisfy these conditions due to serial data correlation, high dimensionality, seasonality, and other complex data structure. Motivated by an application to monitor the ground ozone concentration levels in the Houston-Galveston-Brazoria (HGB) area, we developed a new process monitoring method using principal component analysis and sequential learning. The new method can accommodate high dimensionality, time-varying IC process distribution, serial data correlation, and non-parametric data distribution. It is shown to be a reliable analytic tool for on-line monitoring of air quality.","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"121 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140088159","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}
Mengli Xiao, Haitao Chu, James S. Hodges, Lifeng Lin
{"title":"Quantifying replicability of multiple studies in a meta-analysis","authors":"Mengli Xiao, Haitao Chu, James S. Hodges, Lifeng Lin","doi":"10.1214/23-aoas1806","DOIUrl":"https://doi.org/10.1214/23-aoas1806","url":null,"abstract":"","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"106 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140088655","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}
Siddharth Roy, Anindya Roy, Megan A. Clarke, Ana Gradissimo, Robert D. Burk, Nicolas Wentzensen, Paul S. Albert, Danping Liu
{"title":"Dynamic risk prediction for cervical precancer screening with continuous and binary longitudinal biomarkers","authors":"Siddharth Roy, Anindya Roy, Megan A. Clarke, Ana Gradissimo, Robert D. Burk, Nicolas Wentzensen, Paul S. Albert, Danping Liu","doi":"10.1214/23-aoas1788","DOIUrl":"https://doi.org/10.1214/23-aoas1788","url":null,"abstract":"","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"50 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140085871","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}
Jincheng Shen, Joel Schwartz, Andrea A. Baccarelli, Xihong Lin
{"title":"Testing for the causal mediation effects of multiple mediators using the kernel machine difference method in genome-wide epigenetic studies","authors":"Jincheng Shen, Joel Schwartz, Andrea A. Baccarelli, Xihong Lin","doi":"10.1214/23-aoas1814","DOIUrl":"https://doi.org/10.1214/23-aoas1814","url":null,"abstract":"","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"7 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140091094","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":"A novel estimator of Earth’s curvature (Allowing for inference as well)","authors":"David R. Bell, Olivier Ledoit, Michael Wolf","doi":"10.1214/23-aoas1802","DOIUrl":"https://doi.org/10.1214/23-aoas1802","url":null,"abstract":"","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":" 1069","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140091767","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}
Katherine R. McLaughlin, Lisa G. Johnston, X. Jakupi, D. Gexha-Bunjaku, Edona Deva, M. Handcock
{"title":"Modeling the visibility distribution for respondent-driven sampling with application to population size estimation","authors":"Katherine R. McLaughlin, Lisa G. Johnston, X. Jakupi, D. Gexha-Bunjaku, Edona Deva, M. Handcock","doi":"10.1214/23-aoas1807","DOIUrl":"https://doi.org/10.1214/23-aoas1807","url":null,"abstract":"","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"14 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140087561","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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