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Statistical analysis of longitudinal studies 纵向研究的统计分析
IF 2 3区 数学
International Statistical Review Pub Date : 2022-10-17 DOI: 10.1111/insr.12523
Nan M. Laird
{"title":"Statistical analysis of longitudinal studies","authors":"Nan M. Laird","doi":"10.1111/insr.12523","DOIUrl":"10.1111/insr.12523","url":null,"abstract":"<div>\u0000 \u0000 <p>Longitudinal studies play a prominent role in research on growth, change and/or decline in individuals, and in characterising the environmental and social factors which influence change. The essential feature of a longitudinal study is taking repeated measures of an outcome on the same set of individuals at multiple timepoints, thereby allowing investigators to characterise within subject changes during the measurement period. This paper provides an overview of how the basic design features and analysis of longitudinal studies are related to other study designs, including longitudinal clinical trials as well as repeated measures studies. I summarise the use of the linear mixed model as described in Laird and Ware for the analysis of a broad class of designs and present some applications in health and medicine.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 S1","pages":"S2-S16"},"PeriodicalIF":2.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43109253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
ABC of the future 未来ABC
IF 2 3区 数学
International Statistical Review Pub Date : 2022-10-17 DOI: 10.1111/insr.12522
Henri Pesonen, Umberto Simola, Alvaro Köhn-Luque, Henri Vuollekoski, Xiaoran Lai, Arnoldo Frigessi, Samuel Kaski, David T. Frazier, Worapree Maneesoonthorn, Gael M. Martin, Jukka Corander
{"title":"ABC of the future","authors":"Henri Pesonen,&nbsp;Umberto Simola,&nbsp;Alvaro Köhn-Luque,&nbsp;Henri Vuollekoski,&nbsp;Xiaoran Lai,&nbsp;Arnoldo Frigessi,&nbsp;Samuel Kaski,&nbsp;David T. Frazier,&nbsp;Worapree Maneesoonthorn,&nbsp;Gael M. Martin,&nbsp;Jukka Corander","doi":"10.1111/insr.12522","DOIUrl":"10.1111/insr.12522","url":null,"abstract":"<p>Approximate Bayesian computation (ABC) has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-based statistical models, which are becoming increasingly popular in many research domains. The computational feasibility of ABC for practical applications has been recently boosted by adopting techniques from machine learning to build surrogate models for the approximate likelihood or posterior and by the introduction of a general-purpose software platform with several advanced features, including automated parallelisation. Here we demonstrate the strengths of the advances in ABC by going beyond the typical benchmark examples and considering real applications in astronomy, infectious disease epidemiology, personalised cancer therapy and financial prediction. We anticipate that the emerging success of ABC in producing actual added value and quantitative insights in the real world will continue to inspire a plethora of further applications across different fields of science, social science and technology.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"91 2","pages":"243-268"},"PeriodicalIF":2.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49296447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
A Legacy of EM Algorithms EM算法的遗留问题
IF 2 3区 数学
International Statistical Review Pub Date : 2022-10-12 DOI: 10.1111/insr.12526
Kenneth Lange, Hua Zhou
{"title":"A Legacy of EM Algorithms","authors":"Kenneth Lange,&nbsp;Hua Zhou","doi":"10.1111/insr.12526","DOIUrl":"10.1111/insr.12526","url":null,"abstract":"<div>\u0000 \u0000 <p>Nan Laird has an enormous and growing impact on computational statistics. Her paper with Dempster and Rubin on the expectation-maximisation (EM) algorithm is the second most cited paper in statistics. Her papers and book on longitudinal modelling are nearly as impressive. In this brief survey, we revisit the derivation of some of her most useful algorithms from the perspective of the minorisation-maximisation (MM) principle. The MM principle generalises the EM principle and frees it from the shackles of missing data and conditional expectations. Instead, the focus shifts to the construction of surrogate functions via standard mathematical inequalities. The MM principle can deliver a classical EM algorithm with less fuss or an entirely new algorithm with a faster rate of convergence. In any case, the MM principle enriches our understanding of the EM principle and suggests new algorithms of considerable potential in high-dimensional settings where standard algorithms such as Newton's method and Fisher scoring falter.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 S1","pages":"S52-S66"},"PeriodicalIF":2.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9550131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Simultaneous inference for linear mixed model parameters with an application to small area estimation 线性混合模型参数的同时推理及其在小区域估计中的应用
IF 2 3区 数学
International Statistical Review Pub Date : 2022-09-18 DOI: 10.1111/insr.12519
Katarzyna Reluga, María-José Lombardía, Stefan Sperlich
{"title":"Simultaneous inference for linear mixed model parameters with an application to small area estimation","authors":"Katarzyna Reluga,&nbsp;María-José Lombardía,&nbsp;Stefan Sperlich","doi":"10.1111/insr.12519","DOIUrl":"10.1111/insr.12519","url":null,"abstract":"<p>Over the past decades, linear mixed models have attracted considerable attention in various fields of applied statistics. They are popular whenever clustered, hierarchical or longitudinal data are investigated. Nonetheless, statistical tools for valid simultaneous inference for mixed parameters are rare. This is surprising because one often faces inferential problems beyond the pointwise examination of fixed or mixed parameters. For example, there is an interest in a comparative analysis of cluster-level parameters or subject-specific estimates in studies with repeated measurements. We discuss methods for simultaneous inference assuming a linear mixed model. Specifically, we develop simultaneous prediction intervals as well as multiple testing procedures for mixed parameters. They are useful for joint considerations or comparisons of cluster-level parameters. We employ a consistent bootstrap approximation of the distribution of max-type statistic to construct our tools. The numerical performance of the developed methodology is studied in simulation experiments and illustrated in a data example on household incomes in small areas.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"91 2","pages":"193-217"},"PeriodicalIF":2.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49301545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A Computational Perspective on Projection Pursuit in High Dimensions: Feasible or Infeasible Feature Extraction 高维投影寻踪的计算视角：可行或不可行特征提取
IF 2 3区 数学
International Statistical Review Pub Date : 2022-08-19 DOI: 10.1111/insr.12517
Chunming Zhang, Jimin Ye, Xiaomei Wang
{"title":"A Computational Perspective on Projection Pursuit in High Dimensions: Feasible or Infeasible Feature Extraction","authors":"Chunming Zhang,&nbsp;Jimin Ye,&nbsp;Xiaomei Wang","doi":"10.1111/insr.12517","DOIUrl":"10.1111/insr.12517","url":null,"abstract":"<p>Finding a suitable representation of multivariate data is fundamental in many scientific disciplines. Projection pursuit (\u0000<math>\u0000 <mtext>PP</mtext></math>) aims to extract interesting ‘non-Gaussian’ features from multivariate data, and tends to be computationally intensive even when applied to data of low dimension. In high-dimensional settings, a recent work (Bickel et al., 2018) on \u0000<math>\u0000 <mtext>PP</mtext></math> addresses asymptotic characterization and conjectures of the feasible projections as the dimension grows with sample size. To gain practical utility of and learn theoretical insights into \u0000<math>\u0000 <mtext>PP</mtext></math> in an integral way, data analytic tools needed to evaluate the behaviour of \u0000<math>\u0000 <mtext>PP</mtext></math> in high dimensions become increasingly desirable but are less explored in the literature. This paper focuses on developing computationally fast and effective approaches central to finite sample studies for (i) visualizing the feasibility of \u0000<math>\u0000 <mtext>PP</mtext></math> in extracting features from high-dimensional data, as compared with alternative methods like \u0000<math>\u0000 <mtext>PCA</mtext></math> and \u0000<math>\u0000 <mtext>ICA</mtext></math>, and (ii) assessing the plausibility of \u0000<math>\u0000 <mtext>PP</mtext></math> in cases where asymptotic studies are lacking or unavailable, with the goal of better understanding the practicality, limitation and challenge of \u0000<math>\u0000 <mtext>PP</mtext></math> in the analysis of large data sets.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"91 1","pages":"140-161"},"PeriodicalIF":2.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46111599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference 包含调查抽样、缺失数据分析和因果推断的校准技术
IF 2 3区 数学
International Statistical Review Pub Date : 2022-08-11 DOI: 10.1111/insr.12518
Shixiao Zhang, Peisong Han, Changbao Wu
{"title":"Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference","authors":"Shixiao Zhang,&nbsp;Peisong Han,&nbsp;Changbao Wu","doi":"10.1111/insr.12518","DOIUrl":"10.1111/insr.12518","url":null,"abstract":"<div>\u0000 \u0000 <p>We provide a critical review on calibration methods developed in three different areas: survey sampling, missing data analysis and causal inference. We highlight the connections and variations of calibration techniques used in missing data analysis and causal inference to conventional calibration weighting and estimation in survey sampling and provide a common framework through model-calibration and empirical likelihood to unify different calibration methods proposed in recent literature. The goal is to demonstrate the success and effectiveness of calibration methods in achieving some highly desired properties for missing data analysis and causal inference.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"91 2","pages":"165-192"},"PeriodicalIF":2.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44699065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Administrative Records for Survey Methodology Edited by Asaph Young Chun, Michael D. Larsen, Gabriele Durrant, Jerome P. ReiterJohn Wiley and Sons, 2021, 384 pages, $128.95 (hardcover) ISBN: 978-1-1192-7204-5 《调查方法管理记录》，Asaph Young Chun, Michael D. Larsen, Gabriele Durrant, Jerome P. ReiterJohn Wiley and Sons, 2021, 384页，128.95美元(精装)ISBN: 978-1-1192-7204-5
IF 2 3区 数学
International Statistical Review Pub Date : 2022-07-18 DOI: 10.1111/insr.12516
Reijo Sund
{"title":"Administrative Records for Survey Methodology Edited by Asaph Young Chun, Michael D. Larsen, Gabriele Durrant, Jerome P. ReiterJohn Wiley and Sons, 2021, 384 pages, $128.95 (hardcover) ISBN: 978-1-1192-7204-5","authors":"Reijo Sund","doi":"10.1111/insr.12516","DOIUrl":"10.1111/insr.12516","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 2","pages":"415-417"},"PeriodicalIF":2.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42392630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Extreme Value Theory with Applications to Natural Hazards: From Statistical Theory to Industrial Practice Edited by Nicolas Bousquet and Pietro BernardaraSpringer Cham, 2021, xxii + 481 pages, $199.99 ISBN: 978-3-030-74941-5 极值理论及其在自然灾害中的应用:从统计理论到工业实践，Nicolas Bousquet和Pietro BernardaraSpringer Cham主编，2021,22 + 481页，199.99美元ISBN: 978-3-030- 74945 -5
IF 2 3区 数学
International Statistical Review Pub Date : 2022-07-18 DOI: 10.1111/insr.12513
Fabrizio Durante
{"title":"Extreme Value Theory with Applications to Natural Hazards: From Statistical Theory to Industrial Practice Edited by Nicolas Bousquet and Pietro BernardaraSpringer Cham, 2021, xxii + 481 pages, $199.99 ISBN: 978-3-030-74941-5","authors":"Fabrizio Durante","doi":"10.1111/insr.12513","DOIUrl":"10.1111/insr.12513","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 2","pages":"411-412"},"PeriodicalIF":2.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46430119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Game Data Science , Magy Seif El-Nasr, Truong-Huy D. Nguyen, Alessandro Canossa, Anders DrachenOxford University Press, 2022, xvi + 416 pages, $105 (hardback)/$55 (paperback) ISBN-10: 019289787X, ISBN-13: 978-0192897879 (hardback), 978–0192897886 (paperback) Game Data ScienceMagy SeifEl̴Nasr，Truong‐Huy D.Nguyen，AlessandroCanossa，AndersDrachenOxford University Press，2022，xvi+416页，$105（硬背）/$55（平装本），ISBN‐10:019289787X，ISBN‐13:978‐0192897879（硬背），978–0192897886（平装本）
IF 2 3区 数学
International Statistical Review Pub Date : 2022-07-18 DOI: 10.1111/insr.12514
Shuangzhe Liu
{"title":"Game Data Science , Magy Seif El-Nasr, Truong-Huy D. Nguyen, Alessandro Canossa, Anders DrachenOxford University Press, 2022, xvi + 416 pages, $105 (hardback)/$55 (paperback) ISBN-10: 019289787X, ISBN-13: 978-0192897879 (hardback), 978–0192897886 (paperback)","authors":"Shuangzhe Liu","doi":"10.1111/insr.12514","DOIUrl":"10.1111/insr.12514","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 2","pages":"412-414"},"PeriodicalIF":2.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45307795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Wavelets from a Statistical Perspective Maarten Jansen Chapman and Hall/CRC, 2022, xix + 325 pages, $96 (hardcover) ISBN: 978-1-032-20067-5 (hardcover) Maarten Jansen Chapman and Hall/CRC, 2022, 19 + 325页，96美元(精装)ISBN: 978-1-032-20067-5(精装)
IF 2 3区 数学
International Statistical Review Pub Date : 2022-07-18 DOI: 10.1111/insr.12515
Krzysztof Podgórski
{"title":"Wavelets from a Statistical Perspective Maarten Jansen Chapman and Hall/CRC, 2022, xix + 325 pages, $96 (hardcover) ISBN: 978-1-032-20067-5 (hardcover)","authors":"Krzysztof Podgórski","doi":"10.1111/insr.12515","DOIUrl":"10.1111/insr.12515","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 2","pages":"414-415"},"PeriodicalIF":2.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44357863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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