International Statistical Review最新文献_第6页

The Effect: An Introduction to Research Design and Causality , Nick Huntington-Klein Chapman & Hall/CRC, 2022, xiv + 620 pages, $39.95, paperback. ISBN: 9781032125787 效果：研究设计和因果关系导论NickHuntington‐KleinChapman&Hall/CRC，2022，xiv + 620页，39.95美元，平装本。ISBN:9781032125787

IF 2 3区数学

International Statistical Review Pub Date : 2023-06-21 DOI: 10.1111/insr.12547

Brian W. Sloboda

引用次数: 0

Online Evidential Nearest Neighbour Classification for Internet of Things Time Series 物联网时间序列的在线证据最近邻分类

IF 2 3区数学

International Statistical Review Pub Date : 2023-05-24 DOI: 10.1111/insr.12540

Patrick Toman, N. Ravishanker, S. Rajasekaran, Nathan Lally

{"title":"Online Evidential Nearest Neighbour Classification for Internet of Things Time Series","authors":"Patrick Toman, N. Ravishanker, S. Rajasekaran, Nathan Lally","doi":"10.1111/insr.12540","DOIUrl":"https://doi.org/10.1111/insr.12540","url":null,"abstract":"The ‘Internet of Things’ (IoT) is a rapidly developing set of technologies that leverages large numbers of networked sensors, to relay data in an online fashion. Typically, knowledge of the sensor environment is incomplete and subject to changes over time. There is a need to employ classification algorithms to understand the data. We first review of existing time series classification (TSC) approaches, with emphasis on the well‐known k‐nearest neighbours (kNN) methods. We extend these to dynamical kNN classifiers, and discuss their shortcomings for handling the inherent uncertainty in IoT data. We next review evidential kNN ( EkNN ) classifiers that leverage the well‐known Dempster–Shafer theory to allow principled uncertainty quantification. We develop a dynamic EkNN approach for classifying IoT streams via algorithms that use evidential theoretic pattern rejection rules for (i) classifying incoming patterns into a set of oracle classes, (ii) automatically pruning ambiguously labelled patterns such as aberrant streams (due to malfunctioning sensors, say), and (iii) identifying novel classes that may emerge in new subsequences over time. While these methods have wide applicability in many domains, we illustrate the dynamic kNN and EkNN approaches for classifying a large, noisy IoT time series dataset from an insurance firm.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45000249","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

Increasing Trust in New Data Sources: Crowdsourcing Image Classification for Ecology 增加对新数据源的信任:生态众包图像分类

IF 2 3区数学

International Statistical Review Pub Date : 2023-05-21 DOI: 10.1111/insr.12542

Edgar Santos-Fernandez, Julie Vercelloni, Aiden Price, Grace Heron, Bryce Christensen, Erin E. Peterson, Kerrie Mengersen

{"title":"Increasing Trust in New Data Sources: Crowdsourcing Image Classification for Ecology","authors":"Edgar Santos-Fernandez, Julie Vercelloni, Aiden Price, Grace Heron, Bryce Christensen, Erin E. Peterson, Kerrie Mengersen","doi":"10.1111/insr.12542","DOIUrl":"10.1111/insr.12542","url":null,"abstract":"Crowdsourcing methods facilitate the production of scientific information by non-experts. This form of citizen science (CS) is becoming a key source of complementary data in many fields to inform data-driven decisions and study challenging problems. However, concerns about the validity of these data often constrain their utility. In this paper, we focus on the use of citizen science data in addressing complex challenges in environmental conservation. We consider this issue from three perspectives. First, we present a literature scan of papers that have employed Bayesian models with citizen science in ecology. Second, we compare several popular majority vote algorithms and introduce a Bayesian item response model that estimates and accounts for participants' abilities after adjusting for the difficulty of the images they have classified. The model also enables participants to be clustered into groups based on ability. Third, we apply the model in a case study involving the classification of corals from underwater images from the Great Barrier Reef, Australia. We show that the model achieved superior results in general and, for difficult tasks, a weighted consensus method that uses only groups of experts and experienced participants produced better performance measures. Moreover, we found that participants learn as they have more classification opportunities, which substantially increases their abilities over time. Overall, the paper demonstrates the feasibility of CS for answering complex and challenging ecological questions when these data are appropriately analysed. This serves as motivation for future work to increase the efficacy and trustworthiness of this emerging source of data.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12542","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43727234","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

Correspondence Analysis Using the Cressie–Read Family of Divergence Statistics 使用Cressie-Read分歧统计家族的对应分析

IF 2 3区数学

International Statistical Review Pub Date : 2023-05-15 DOI: 10.1111/insr.12541

Eric J. Beh, Rosaria Lombardo

引用次数: 0

An interview with Luis Raúl Pericchi 采访路易斯Raúl伯里基

IF 2 3区数学

International Statistical Review Pub Date : 2023-03-21 DOI: 10.1111/insr.12537

Abel Rodríguez, Bruno Sansó

{"title":"An interview with Luis Raúl Pericchi","authors":"Abel Rodríguez, Bruno Sansó","doi":"10.1111/insr.12537","DOIUrl":"10.1111/insr.12537","url":null,"abstract":"<div>\u0000 \u0000 Luis Raúl Pericchi Guerra was born in Caracas, Venezuela, on 11 March 1952. He completed a B.S. in Mathematics in 1975 at the Universidad Simón Bolívar in Caracas, an M.S. in Statistics at the University of California Berkeley in 1978 and a Ph.D. in Statistics at Imperial College London in 1981. After graduating from Imperial College, Luis Raúl went back to Universidad Simón Bolívar. There, he played a key role in the developing of graduate programmes in Statistics and single handedly built an internationally recognised group focused on Bayesian statistics. In 2001, he moved to the Universidad de Puerto Rico in Rio Piedras to become the Chair of the Mathematics Department. At Universidad de Puerto Rico, he was instrumental in the establishment of a Ph.D. track in Computational Mathematics and Statistics. Luis Raúl has published over 120 papers in statistical and domain-specific journals, making significant contributions to several areas of Bayesian statistics (especially in the areas of model selection and Bayesian robustness) and their application (especially in hydrology). He is a Fellow of the American Statistical Association, the International Society for Bayesian Analysis, the John Simon Guggenheim Memorial Foundation and an Elected Member of the International Statistical Institute. This conversation took place over multiple sessions during the 2022 O'Bayes meeting in Santa Cruz, California, and the months that followed.\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48516077","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

Estimating the Reciprocal of a Binomial Proportion 估计二项式比例的倒数

IF 2 3区数学

International Statistical Review Pub Date : 2023-03-20 DOI: 10.1111/insr.12539

Jiajin Wei, Ping He, Tiejun Tong

{"title":"Estimating the Reciprocal of a Binomial Proportion","authors":"Jiajin Wei, Ping He, Tiejun Tong","doi":"10.1111/insr.12539","DOIUrl":"10.1111/insr.12539","url":null,"abstract":"The binomial proportion is a classic parameter with many applications and has also been extensively studied in the literature. By contrast, the reciprocal of the binomial proportion, or the inverse proportion, is often overlooked, even though it also plays an important role in various fields. To estimate the inverse proportion, the maximum likelihood method fails to yield a valid estimate when there is no successful event in the Bernoulli trials. To overcome this zero-event problem, several methods have been introduced in the previous literature. Yet to the best of our knowledge, there is little work on a theoretical comparison of the existing estimators. In this paper, we first review some commonly used estimators for the inverse proportion, study their asymptotic properties, and then develop a new estimator that aims to eliminate the estimation bias. We further conduct Monte Carlo simulations to compare the finite sample performance of the existing and new estimators, and also apply them to handle the zero-event problem in a meta-analysis of COVID-19 data for assessing the relative risks of physical distancing on the infection of coronavirus.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12539","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47877923","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

Social Networks: Modelling and Analysis Niyati Aggrawal and Adarsh AnandCRC Press, 2022, xvii + 235 pages, hardcover ($170); e-book ($44.21). ISBN: 978-0-367-54139-2 (hardcover), 978-0-367-54173-6 (paperback), 978-1-003-08806-6 (e-book) 社交网络：建模和分析Niyati Aggrawal和Adarsh AnandCRC出版社，2022年，xvii+235页，硬拷贝（170美元）；电子书（44.21美元）。ISBN:978-0-367-54139-2（硬拷贝）、978-0-367-54173-6（平装本）、978-1-003-08806-6（电子书）

IF 2 3区数学

International Statistical Review Pub Date : 2023-03-13 DOI: 10.1111/insr.12538

Arindam Sengupta

引用次数: 0

Social Networks: Modelling and AnalysisNiyatiAggrawal and AdarshAnandCRC Press, 2022, xvii + 235 pages, hardcover ($170); e‐book ($44.21). ISBN: 78‐0‐367‐54139‐2 (hardcover), 978‐0‐367‐54173‐6 (paperback), 978‐1‐003‐08806‐6 (e‐book) 社交网络:建模和分析niyatiaggrawal和AdarshAnandCRC出版社，2022,xvii + 235页，精装(170美元);e书(44.21美元)。ISBN: 78‐0‐367‐54139‐2(精装)，978‐0‐367‐54173‐6(平装)，978‐1‐003‐08806‐6(电子书)

IF 2 3区数学

International Statistical Review Pub Date : 2023-03-13 DOI: 10.1111/insr.12538

A. Sengupta

引用次数: 0

Optimal Treatment Regimes: A Review and Empirical Comparison 最佳治疗方案:综述与实证比较

IF 2 3区数学

International Statistical Review Pub Date : 2023-02-22 DOI: 10.1111/insr.12536

Z. Li, Jie Chen, Eric B. Laber, Fang Liu, Richard Baumgartner

{"title":"Optimal Treatment Regimes: A Review and Empirical Comparison","authors":"Z. Li, Jie Chen, Eric B. Laber, Fang Liu, Richard Baumgartner","doi":"10.1111/insr.12536","DOIUrl":"https://doi.org/10.1111/insr.12536","url":null,"abstract":"A treatment regime is a sequence of decision rules, one per decision point, that maps accumulated patient information to a recommended intervention. An optimal treatment regime maximises expected cumulative utility if applied to select interventions in a population of interest. As a treatment regime seeks to improve the quality of healthcare by individualising treatment, it can be viewed as an approach to formalising precision medicine. Increased interest and investment in precision medicine has led to a surge of methodological research focusing on estimation and evaluation of optimal treatment regimes from observational and/or randomised studies. These methods are becoming commonplace in biomedical research, although guidance about how to choose among existing methods in practice has been somewhat limited. The purpose of this review is to describe some of the most commonly used methods for estimation of an optimal treatment regime, and to compare these estimators in a series of simulation experiments and applications to real data. The results of these simulations along with the theoretical/methodological properties of these estimators are used to form recommendations for applied researchers.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45836106","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: 3

A Statistical Review of Template Model Builder: A Flexible Tool for Spatial Modelling 模板模型生成器的统计回顾:一个灵活的空间建模工具

IF 2 3区数学

International Statistical Review Pub Date : 2022-12-18 DOI: 10.1111/insr.12534

Aaron Osgood-Zimmerman, Jon Wakefield

{"title":"A Statistical Review of Template Model Builder: A Flexible Tool for Spatial Modelling","authors":"Aaron Osgood-Zimmerman, Jon Wakefield","doi":"10.1111/insr.12534","DOIUrl":"10.1111/insr.12534","url":null,"abstract":"<div>\u0000 \u0000 The integrated nested Laplace approximation (INLA) is a well-known and popular technique for spatial modelling with a user-friendly interface in the R-INLA package. Unfortunately, only a certain class of latent Gaussian models are amenable to fitting with INLA. In this paper, we review template model builder (TMB), an existing technique and software package which is well-suited to fitting complex spatio-temporal models. TMB is relatively unknown to the spatial statistics community, but it is a flexible random effects modelling tool which allows users to define customizable and complex mixed effects models through C++ templates. After contrasting the methodology behind TMB with INLA, we provide a large-scale simulation study assessing and comparing R-INLA and TMB for continuous spatial models, fitted via the stochastic partial differential equations (SPDE) approximation. The results show that the predictive fields from both methods are comparable in most situations even though TMB estimates for fixed or random effects may have slightly larger bias than R-INLA. We also present a smaller discrete spatial simulation study, in which both approaches perform well. We conclude with a joint analysis of breast cancer incidence and mortality data implemented in TMB which requires a model which cannot be fit with R-INLA.\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48833529","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