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Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation 通过辅助均匀化的可扩展贝叶斯多变更点检测
IF 2 3区 数学
International Statistical Review Pub Date : 2022-06-15 DOI: 10.1111/insr.12511
Lu Shaochuan
{"title":"Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation","authors":"Lu Shaochuan","doi":"10.1111/insr.12511","DOIUrl":"10.1111/insr.12511","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we perform a sparse filtering recursion for efficient changepoint detection for discrete-time observations. We attach auxiliary event times to the chronologically ordered observations and formulate multiple changepoint problems of discrete-time observations into continuous-time observations. Ideally, both the computational and memory costs of the proposed auxiliary uniformisation forward-filtering backward-sampling algorithm can be quadratically scaled down to the number of changepoints instead of the number of observations, which would otherwise be prohibitive for a long sequence of observations. To avoid model bias, a time-varying changepoint recurrence rate across different segments is assumed to characterise diverse scales of run lengths of the changepoints. We demonstrate the methods through simulation studies and real data analysis.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"91 1","pages":"88-113"},"PeriodicalIF":2.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46806947","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
Diagnostic Tests for the Necessity of Weight in Regression With Survey Data 调查数据回归中权重必要性的诊断检验
IF 2 3区 数学
International Statistical Review Pub Date : 2022-06-09 DOI: 10.1111/insr.12509
Feng Wang, HaiYing Wang, Jun Yan
{"title":"Diagnostic Tests for the Necessity of Weight in Regression With Survey Data","authors":"Feng Wang,&nbsp;HaiYing Wang,&nbsp;Jun Yan","doi":"10.1111/insr.12509","DOIUrl":"10.1111/insr.12509","url":null,"abstract":"<div>\u0000 \u0000 <p>To weight or not to weight in regression analyses with survey data has been debated in the literature. The problem is essentially a tradeoff between the bias and the variance of the regression coefficient estimator. An array of diagnostic tests for informative weights have been developed. Nonetheless, studies comparing the performance of the tests, especially for finite samples, are scarce, and the theoretical equivalence of some tests has not been investigated. Focusing on the linear regression setting, we review a collection of such tests and propose enhanced versions of some of them that require an auxiliary regression model for the weight. Further, the equivalence of two popular tests is established which has not been reported before. In contrast to existing reviews with no empirical comparison, we compare the sizes and powers of the tests in simulation studies. The reviewed tests are applied to a regression analysis of the family expenditure using the data from the China Family Panel Study.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"91 1","pages":"55-71"},"PeriodicalIF":2.0,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42527094","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
From Pareto to Weibull – A Constructive Review of Distributions on ℝ+ 从Pareto到Weibull——关于ℝ+
IF 2 3区 数学
International Statistical Review Pub Date : 2022-06-06 DOI: 10.1111/insr.12508
Corinne Sinner, Yves Dominicy, Julien Trufin, Wout Waterschoot, Patrick Weber, Christophe Ley
{"title":"From Pareto to Weibull – A Constructive Review of Distributions on ℝ+","authors":"Corinne Sinner,&nbsp;Yves Dominicy,&nbsp;Julien Trufin,&nbsp;Wout Waterschoot,&nbsp;Patrick Weber,&nbsp;Christophe Ley","doi":"10.1111/insr.12508","DOIUrl":"10.1111/insr.12508","url":null,"abstract":"<div>\u0000 \u0000 <p>Power laws and power laws with exponential cut-off are two distinct families of distributions on the positive real half-line. In the present paper, we propose a unified treatment of both families by building a family of distributions that interpolates between them, which we call Interpolating Family (IF) of distributions. Our original construction, which relies on techniques from statistical physics, provides a connection for hitherto unrelated distributions like the Pareto and Weibull distributions, and sheds new light on them. The IF also contains several distributions that are neither of power law nor of power law with exponential cut-off type. We calculate quantile-based properties, moments and modes for the IF. This allows us to review known properties of famous distributions on \u0000<math>\u0000 <msup>\u0000 <mrow>\u0000 <mi>ℝ</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>+</mo>\u0000 </mrow>\u0000 </msup></math> and to provide in a single sweep these characteristics for various less known (and new) special cases of our Interpolating Family.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"91 1","pages":"35-54"},"PeriodicalIF":2.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46009017","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
Using Survey Sampling Algorithms For Exact Inference in Logistic Regression 在Logistic回归中使用调查抽样算法进行精确推理
IF 2 3区 数学
International Statistical Review Pub Date : 2022-05-31 DOI: 10.1111/insr.12507
Louis-Paul Rivest, Serigne Abib Gaye
{"title":"Using Survey Sampling Algorithms For Exact Inference in Logistic Regression","authors":"Louis-Paul Rivest,&nbsp;Serigne Abib Gaye","doi":"10.1111/insr.12507","DOIUrl":"10.1111/insr.12507","url":null,"abstract":"<div>\u0000 \u0000 <p>Several exact inference procedures for logistic regression require the simulation of a 0-1 dependent vector according to its conditional distribution, given the sufficient statistics for some nuisance parameters. This is viewed, in this work, as a sampling problem involving a population of \u0000<math>\u0000 <mi>n</mi></math> units, unequal selection probabilities and balancing constraints. The basis for this reformulation of exact inference is a proposition deriving the limit, as \u0000<math>\u0000 <mi>n</mi></math> goes to infinity, of the conditional distribution of the dependent vector given the logistic regression sufficient statistics. It is proposed to sample from this distribution using the cube sampling algorithm. The interest of this approach to exact inference is illustrated by tackling new problems. First it allows to carry out exact inference with continuous covariates. It is also useful for the investigation of a partial correlation between several 0-1 vectors. This is illustrated in an example dealing with presence-absence data in ecology.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"91 1","pages":"18-34"},"PeriodicalIF":2.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42562594","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
Some Solutions Inspired by Survey Sampling Theory to Build Effective Clinical Trials 调查抽样理论对构建有效临床试验的启示
IF 2 3区 数学
International Statistical Review Pub Date : 2022-04-10 DOI: 10.1111/insr.12498
Yves Tillé
{"title":"Some Solutions Inspired by Survey Sampling Theory to Build Effective Clinical Trials","authors":"Yves Tillé","doi":"10.1111/insr.12498","DOIUrl":"https://doi.org/10.1111/insr.12498","url":null,"abstract":"The organisation of a design of experiments, for example, for the realisation of a clinical trial, is crucial. It is often desirable to balance designs so that the means of the covariates are approximately the same in the test and control groups. In survey sampling theory, balanced sampling and calibration are two techniques that improve the precision of estimates. In this paper, we show the links between the two areas. We begin by assessing the gain in precision between a balanced design and a simple random sampling for the least squares estimators and the estimator by differences. We compare rerandomisation techniques and the cube method in order to balance the design. We propose a new method, particularly efficient, which combines the cube method with multivariate matching. A set of simulations is carried out in order to evaluate the different methods. The interest of the calibration is shown even if the design is almost balanced. It is thus shown that tools used by survey statisticians can be useful for experimental designs and clinical trials.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 1","pages":"481 - 498"},"PeriodicalIF":2.0,"publicationDate":"2022-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42827426","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
Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms 偏见，公平和问责与人工智能和机器学习算法
IF 2 3区 数学
International Statistical Review Pub Date : 2022-04-10 DOI: 10.1111/insr.12492
Nengfeng Zhou, Zach Zhang, Vijayan N. Nair, Harsh Singhal, Jie Chen
{"title":"Bias, Fairness and Accountability with Artificial Intelligence and Machine Learning Algorithms","authors":"Nengfeng Zhou,&nbsp;Zach Zhang,&nbsp;Vijayan N. Nair,&nbsp;Harsh Singhal,&nbsp;Jie Chen","doi":"10.1111/insr.12492","DOIUrl":"10.1111/insr.12492","url":null,"abstract":"<div>\u0000 \u0000 <p>The advent of artificial intelligence (AI) and machine learning algorithms has led to opportunities as well as challenges in their use. In this overview paper, we begin with a discussion of bias and fairness issues that arise with the use of AI techniques, with a focus on supervised machine learning algorithms. We then describe the types and sources of data bias and discuss the nature of algorithmic unfairness. In addition, we provide a review of fairness metrics in the literature, discuss their limitations, and describe de-biasing (or mitigation) techniques in the model life cycle.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 3","pages":"468-480"},"PeriodicalIF":2.0,"publicationDate":"2022-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45085727","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: 6
Communicating with Data: The Art of Writing for Data Science Deborah Nolan and Sara Stoudt Oxford University Press, 2021, vii + 331 pages, $45.95, paperback ISBN: 978-0-1988-6275-8 与数据沟通:数据科学写作的艺术Deborah Nolan and Sara Stoudt牛津大学出版社，2021,vii + 331页，45.95美元，平装ISBN: 978-0-1988-6275-8
IF 2 3区 数学
International Statistical Review Pub Date : 2022-03-15 DOI: 10.1111/insr.12496
Kelly McConville
{"title":"Communicating with Data: The Art of Writing for Data Science Deborah Nolan and Sara Stoudt Oxford University Press, 2021, vii + 331 pages, $45.95, paperback ISBN: 978-0-1988-6275-8","authors":"Kelly McConville","doi":"10.1111/insr.12496","DOIUrl":"10.1111/insr.12496","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 1","pages":"189-191"},"PeriodicalIF":2.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44294305","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: 5
Replication and Evidence Factors in Observational Studies Paul R. Rosenbaum Chapman & Hall/CRC, 2021, xviii + 254 pages, $120, hardback ISBN: 978-036748-388-3 Paul R. Rosenbaum Chapman & Hall/CRC, 2021, xviii + 254页，$120，精装本ISBN: 978-036748-388-3
IF 2 3区 数学
International Statistical Review Pub Date : 2022-03-14 DOI: 10.1111/insr.12495
John H. Maindonald
{"title":"Replication and Evidence Factors in Observational Studies Paul R. Rosenbaum Chapman & Hall/CRC, 2021, xviii + 254 pages, $120, hardback ISBN: 978-036748-388-3","authors":"John H. Maindonald","doi":"10.1111/insr.12495","DOIUrl":"10.1111/insr.12495","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 1","pages":"187-188"},"PeriodicalIF":2.0,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48012945","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
Population Genomics with R Emmanuel Paradis Chapman & Hall/CRC, 2020, 394 pages, $120, hardback ISBN: 978-1-1386-0818-4 人口基因组学与R Emmanuel Paradis Chapman & Hall/CRC, 2020, 394页，$120，精装本ISBN: 978-1-1386-0818-4
IF 2 3区 数学
International Statistical Review Pub Date : 2022-03-13 DOI: 10.1111/insr.12493
Daniel Fischer
{"title":"Population Genomics with R Emmanuel Paradis Chapman & Hall/CRC, 2020, 394 pages, $120, hardback ISBN: 978-1-1386-0818-4","authors":"Daniel Fischer","doi":"10.1111/insr.12493","DOIUrl":"10.1111/insr.12493","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 1","pages":"184-185"},"PeriodicalIF":2.0,"publicationDate":"2022-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48935840","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
Fundamentals of Causal Inference with R Babette A. Brumback Chapman & Hall/CRC, 2021, xi + 236 pages, $69.95, hardcover ISBN: 978-0-3677-0505-3 《因果推理基础》(R Babette A. Brumback Chapman & Hall/CRC, 2021, 11 + 236页，69.95美元，精装ISBN: 978-0-3677-0505-3)
IF 2 3区 数学
International Statistical Review Pub Date : 2022-03-13 DOI: 10.1111/insr.12494
Debashis Ghosh
{"title":"Fundamentals of Causal Inference with R Babette A. Brumback Chapman & Hall/CRC, 2021, xi + 236 pages, $69.95, hardcover ISBN: 978-0-3677-0505-3","authors":"Debashis Ghosh","doi":"10.1111/insr.12494","DOIUrl":"10.1111/insr.12494","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 1","pages":"185-186"},"PeriodicalIF":2.0,"publicationDate":"2022-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48182731","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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