{"title":"An Introduction to R and Python for Data Analysis: A Side-by-Side Approach Taylor, R. Brown Chapman and Hall/CRC, 2023, 246 pages (hardback $99.95, ebook $74.96) ISBN 978-10322032-56","authors":"Daniel Fischer","doi":"10.1111/insr.12568","DOIUrl":"10.1111/insr.12568","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140073303","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}
Federico P. Cortese, Fulvia Pennoni, Francesco Bartolucci
We propose a multivariate regime switching model based on a Student- copula function with parameters controlling the strength of correlation between variables and that are governed by a latent Markov process. To estimate model parameters by maximum likelihood, we consider a two-step procedure carried out through the Expectation–Maximisation algorithm. To address the main computational burden related to the estimation of the matrix of dependence parameters and the number of degrees of freedom of the Student- copula, we show a novel use of the Lagrange multipliers, which simplifies the estimation process. The simulation study shows that the estimators have good finite sample properties and the estimation procedure is computationally efficient. An application concerning log-returns of five cryptocurrencies shows that the model permits identifying bull and bear market periods based on the intensity of the correlations between crypto assets.
我们提出了一种基于 Student- t copula 函数的多变量制度转换模型，其参数控制着变量之间的相关性强度，并受潜在马尔可夫过程的支配。为了用最大似然法估计模型参数，我们考虑通过期望最大化算法分两步进行。为了解决与估计依赖性参数矩阵和 Student- t 协程自由度数相关的主要计算负担，我们展示了拉格朗日乘数的一种新用法，它简化了估计过程。模拟研究表明，估计器具有良好的有限样本特性，估计过程的计算效率也很高。有关五种加密货币对数收益率的应用表明，该模型可以根据加密资产之间的相关性强度来识别牛市和熊市时期。
{"title":"Maximum Likelihood Estimation of Multivariate Regime Switching Student-t Copula Models","authors":"Federico P. Cortese, Fulvia Pennoni, Francesco Bartolucci","doi":"10.1111/insr.12562","DOIUrl":"https://doi.org/10.1111/insr.12562","url":null,"abstract":"<p>We propose a multivariate regime switching model based on a Student-\u0000<span></span><math>\u0000 <mi>t</mi></math> copula function with parameters controlling the strength of correlation between variables and that are governed by a latent Markov process. To estimate model parameters by maximum likelihood, we consider a two-step procedure carried out through the Expectation–Maximisation algorithm. To address the main computational burden related to the estimation of the matrix of dependence parameters and the number of degrees of freedom of the Student-\u0000<span></span><math>\u0000 <mi>t</mi></math> copula, we show a novel use of the Lagrange multipliers, which simplifies the estimation process. The simulation study shows that the estimators have good finite sample properties and the estimation procedure is computationally efficient. An application concerning log-returns of five cryptocurrencies shows that the model permits identifying bull and bear market periods based on the intensity of the correlations between crypto assets.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579739","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}
Marcelo Bourguignon, Diego I. Gallardo, Helton Saulo
In this paper, we develop a fully parametric quantile regression model based on the generalised three-parameter beta (GB3) distribution. Beta regression models are primarily used to model rates and proportions. However, these models are usually specified in terms of a conditional mean. Therefore, they may be inadequate if the observed response variable follows an asymmetrical distribution. In addition, beta regression models do not consider the effect of the covariates across the spectrum of the dependent variable, which is possible through the conditional quantile approach. In order to introduce the proposed GB3 regression model, we first reparameterise the GB3 distribution by inserting a quantile parameter, and then we develop the new proposed quantile model. We also propose a simple interpretation of the predictor–response relationship in terms of percentage increases/decreases of the quantile. A Monte Carlo study is carried out for evaluating the performance of the maximum likelihood estimates and the choice of the link functions. Finally, a real COVID-19 dataset from Chile is analysed and discussed to illustrate the proposed approach.
{"title":"Parametric Quantile Beta Regression Model","authors":"Marcelo Bourguignon, Diego I. Gallardo, Helton Saulo","doi":"10.1111/insr.12564","DOIUrl":"10.1111/insr.12564","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we develop a fully parametric quantile regression model based on the generalised three-parameter beta (GB3) distribution. Beta regression models are primarily used to model rates and proportions. However, these models are usually specified in terms of a conditional mean. Therefore, they may be inadequate if the observed response variable follows an asymmetrical distribution. In addition, beta regression models do not consider the effect of the covariates across the spectrum of the dependent variable, which is possible through the conditional quantile approach. In order to introduce the proposed GB3 regression model, we first reparameterise the GB3 distribution by inserting a quantile parameter, and then we develop the new proposed quantile model. We also propose a simple interpretation of the predictor–response relationship in terms of percentage increases/decreases of the quantile. A Monte Carlo study is carried out for evaluating the performance of the maximum likelihood estimates and the choice of the link functions. Finally, a real COVID-19 dataset from Chile is analysed and discussed to illustrate the proposed approach.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139967598","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}
Huy N. Chau, J. Lars Kirkby, Dang H. Nguyen, Duy Nguyen, Nhu N. Nguyen, Thai Nguyen
In this paper, we survey the recent development of inversion-free Newton's method, which directly avoids computing the inversion of Hessian, and demonstrate its applications in estimating parameters of models such as linear and logistic regression. A detailed review of existing methodology is provided, along with comparisons of various competing algorithms. We provide numerical examples that highlight some deficiencies of existing approaches, and demonstrate how the inversion-free methods can improve performance. Motivated by recent works in literature, we provide a unified subsampling framework that can be combined with the inversion-free Newton's method to estimate model parameters including those of linear and logistic regression. Numerical examples are provided for illustration.
{"title":"On the Inversion-Free Newton's Method and Its Applications","authors":"Huy N. Chau, J. Lars Kirkby, Dang H. Nguyen, Duy Nguyen, Nhu N. Nguyen, Thai Nguyen","doi":"10.1111/insr.12563","DOIUrl":"10.1111/insr.12563","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we survey the recent development of inversion-free Newton's method, which directly avoids computing the inversion of Hessian, and demonstrate its applications in estimating parameters of models such as linear and logistic regression. A detailed review of existing methodology is provided, along with comparisons of various competing algorithms. We provide numerical examples that highlight some deficiencies of existing approaches, and demonstrate how the inversion-free methods can improve performance. Motivated by recent works in literature, we provide a unified subsampling framework that can be combined with the inversion-free Newton's method to estimate model parameters including those of linear and logistic regression. Numerical examples are provided for illustration.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139666473","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}
Auxiliary variables with known population totals are extensively used in survey sampling to construct generalised regression (GR) estimators or optimal regression (OR) estimators of totals or means of study variables. This article explores the possibility of improving the efficiency of such estimators when continuous auxiliary variables are used in the regression estimation jointly with appropriate power functions of them, provided that the values of the auxiliary variables are known for all units in the population. The efficiency gain is determined analytically in the case of the OR estimator. A practical criterion for choosing the power functions that maximise the efficiency gain, involving the coefficient of determination in the regression fit of the study variable, is proposed for both the OR estimation and the more practicable, but generally less efficient, GR estimation. Furthermore, the effect of adding a power function of a continuous auxiliary variable in regression estimation is investigated when this variable is also used at the design stage. A simulation study shows that the joint use of a continuous auxiliary variable and a power function of it chosen according to the proposed criterion may improve considerably the efficiency of OR estimation, and much more the efficiency of GR estimation.
在调查抽样中，已知总体的辅助变量被广泛用于构建研究变量总体或均值的广义回归（GR）估计值或最优回归（OR）估计值。本文探讨了当连续辅助变量与适当的幂函数一起用于回归估计时，如果辅助变量的值是已知的，则可以提高此类估计器的效率。在 OR 估计器的情况下，效率增益是通过分析确定的。针对 OR 估计和更实用但通常效率较低的 GR 估计，提出了一个实用标准，用于选择能使效率增益最大化的幂函数，该标准涉及研究变量回归拟合的决定系数。此外，还研究了在回归估计中添加连续辅助变量的幂函数的效果，当该变量也在设计阶段使用时。模拟研究表明，联合使用连续辅助变量和根据所提标准选择的幂函数，可以显著提高 OR 估计的效率，并大大提高 GR 估计的效率。
{"title":"On the Most Effective Use of Continuous Auxiliary Variables in Regression Estimation in Survey Sampling","authors":"Takis Merkouris","doi":"10.1111/insr.12561","DOIUrl":"10.1111/insr.12561","url":null,"abstract":"<div>\u0000 \u0000 <p>Auxiliary variables with known population totals are extensively used in survey sampling to construct generalised regression (GR) estimators or optimal regression (OR) estimators of totals or means of study variables. This article explores the possibility of improving the efficiency of such estimators when continuous auxiliary variables are used in the regression estimation jointly with appropriate power functions of them, provided that the values of the auxiliary variables are known for all units in the population. The efficiency gain is determined analytically in the case of the OR estimator. A practical criterion for choosing the power functions that maximise the efficiency gain, involving the coefficient of determination in the regression fit of the study variable, is proposed for both the OR estimation and the more practicable, but generally less efficient, GR estimation. Furthermore, the effect of adding a power function of a continuous auxiliary variable in regression estimation is investigated when this variable is also used at the design stage. A simulation study shows that the joint use of a continuous auxiliary variable and a power function of it chosen according to the proposed criterion may improve considerably the efficiency of OR estimation, and much more the efficiency of GR estimation.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944756","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}
This paper briefly reviews the recent research in matrix-variate time series analysis, discusses some new developments, especially for seasonal time series, and demonstrates some applications. A general matrix autoregressive moving-average model is introduced. The paper narrates a simple approach for understanding the model, identifiability issues, and estimation. Real examples are used to illustrate the theory.
{"title":"Matrix-Variate Time Series Analysis: A Brief Review and Some New Developments","authors":"Ruey S. Tsay","doi":"10.1111/insr.12558","DOIUrl":"10.1111/insr.12558","url":null,"abstract":"<p>This paper briefly reviews the recent research in matrix-variate time series analysis, discusses some new developments, especially for seasonal time series, and demonstrates some applications. A general matrix autoregressive moving-average model is introduced. The paper narrates a simple approach for understanding the model, identifiability issues, and estimation. Real examples are used to illustrate the theory.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135974289","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}
Leontine Alkema, Thomas Brendan Murphy, Adrian E. Raftery
Summary Professor Adrian E. Raftery is the Boeing International Professor of Statistics and Sociology and an adjunct professor of Atmospheric Sciences at the University of Washington in Seattle. He was born in Dublin, Ireland, and obtained a BA in Mathematics (1976) and an MSc in Statistics and Operations Research (1977) at Trinity College Dublin. He obtained a doctorate in mathematical statistics in 1980 from the Université Pierre et Marie Curie in Paris, France, under the supervision of Paul Deheuvels. He was a lecturer in statistics at Trinity College Dublin from 1980 to 1986, and then an associate (1986–1990) and full (1990‐present) professor of statistics and sociology at the University of Washington. He was the founding Director of the Center for Statistics and Social Sciences (1999–2009). Professor Raftery has published over 200 articles in peer‐reviewed statistical, sociological and other journals. His research focuses on Bayesian model selection and Bayesian model averaging, model‐based clustering, inference for deterministic simulation models, and the development of new statistical methods for demography, sociology, and the environmental and health sciences. He is a member of the United States National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences, an Honorary Member of the Royal Irish Academy, a member of the Washington State Academy of Sciences, a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an elected Member of the Sociological Research Association. He has won the Population Association of America's Clifford C. Clogg Award, the American Sociological Association's Paul F. Lazarsfeld Award for Distinguished Contribution to Knowledge, the Jerome Sacks Award for Outstanding Cross‐Disciplinary Research from the National Institute of Statistical Sciences, the Parzen Prize for Statistical Innovation, and the Science Foundation Ireland St. Patrick's Day Medal. He is also a former Coordinating and Applications Editor of the Journal of the American Statistical Association and a former Editor of Sociological Methodology. He was identified as the world's most cited researcher in mathematics for the decade 1995–2005 by Thomson‐ISI. Thirty‐three students have obtained PhD's working under Raftery's supervision, of whom 21 hold or have held tenure‐track university faculty positions. He has over 150 academic descendants. This interview took place over two sessions in March 2023.
阿德里安·e·拉特里教授是波音统计和社会学国际教授，也是西雅图华盛顿大学大气科学兼职教授。他出生于爱尔兰都柏林，1976年在都柏林三一学院获得数学学士学位，1977年获得统计与运筹学硕士学位。1980年，在Paul deheuvel的指导下，他在法国巴黎的Pierre et Marie Curie大学获得数理统计博士学位。1980年至1986年，他在都柏林圣三一学院(Trinity College Dublin)担任统计学讲师，随后在华盛顿大学(University of Washington)担任副教授(1986年至1990年)和统计学和社会学正教授(1990年至今)。他是统计与社会科学中心的创始主任(1999-2009)。拉特里教授在同行评议的统计学、社会学和其他期刊上发表了200多篇文章。他的研究主要集中在贝叶斯模型选择和贝叶斯模型平均，基于模型的聚类，确定性模拟模型的推理，以及人口学，社会学，环境和健康科学的新统计方法的发展。他是美国国家科学院院士、美国艺术与科学院院士、皇家爱尔兰学院荣誉院士、华盛顿州科学院院士、美国统计协会院士、数学统计研究所院士、社会学研究协会当选会员。他曾获得美国人口协会Clifford C. Clogg奖、美国社会学协会Paul F. Lazarsfeld杰出知识贡献奖、美国国家统计科学研究所Jerome Sacks杰出跨学科研究奖、Parzen统计创新奖和科学基金会爱尔兰圣帕特里克节奖章。他还是《美国统计协会杂志》的前协调与应用编辑，以及《社会学方法论》的前编辑。他被Thomson‐ISI评为1995-2005年间世界上被引用次数最多的数学研究者。33名学生在ravary的指导下获得了博士学位，其中21人担任或曾担任终身教职。他有150多名学者后裔。这次采访在2023年3月分两次进行。
{"title":"Interview With Adrian Raftery","authors":"Leontine Alkema, Thomas Brendan Murphy, Adrian E. Raftery","doi":"10.1111/insr.12557","DOIUrl":"https://doi.org/10.1111/insr.12557","url":null,"abstract":"Summary Professor Adrian E. Raftery is the Boeing International Professor of Statistics and Sociology and an adjunct professor of Atmospheric Sciences at the University of Washington in Seattle. He was born in Dublin, Ireland, and obtained a BA in Mathematics (1976) and an MSc in Statistics and Operations Research (1977) at Trinity College Dublin. He obtained a doctorate in mathematical statistics in 1980 from the Université Pierre et Marie Curie in Paris, France, under the supervision of Paul Deheuvels. He was a lecturer in statistics at Trinity College Dublin from 1980 to 1986, and then an associate (1986–1990) and full (1990‐present) professor of statistics and sociology at the University of Washington. He was the founding Director of the Center for Statistics and Social Sciences (1999–2009). Professor Raftery has published over 200 articles in peer‐reviewed statistical, sociological and other journals. His research focuses on Bayesian model selection and Bayesian model averaging, model‐based clustering, inference for deterministic simulation models, and the development of new statistical methods for demography, sociology, and the environmental and health sciences. He is a member of the United States National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences, an Honorary Member of the Royal Irish Academy, a member of the Washington State Academy of Sciences, a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an elected Member of the Sociological Research Association. He has won the Population Association of America's Clifford C. Clogg Award, the American Sociological Association's Paul F. Lazarsfeld Award for Distinguished Contribution to Knowledge, the Jerome Sacks Award for Outstanding Cross‐Disciplinary Research from the National Institute of Statistical Sciences, the Parzen Prize for Statistical Innovation, and the Science Foundation Ireland St. Patrick's Day Medal. He is also a former Coordinating and Applications Editor of the Journal of the American Statistical Association and a former Editor of Sociological Methodology. He was identified as the world's most cited researcher in mathematics for the decade 1995–2005 by Thomson‐ISI. Thirty‐three students have obtained PhD's working under Raftery's supervision, of whom 21 hold or have held tenure‐track university faculty positions. He has over 150 academic descendants. This interview took place over two sessions in March 2023.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135166206","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}
Daryan Naatjes, Stephen A. Sedory, Sarjinder Singh
Summary In this paper, we introduce two new randomised response models for estimating the prevalence of two sensitive characteristics and their overlap in a population by making use of a single deck of cards. The proposed models ensure the privacy of the respondents and also reduce the burden on the respondents as they require the random selection of only one card from a deck of cards each of which contains a pair of questions that are to be answered in order. The variance expressions of the proposed estimators are derived and matched to their Cramer–Rao lower bounds of variances. A simulation study has been carried out to compare the proposed models to each other for least protection. Lastly, a real survey application, related to the acceptability of the vaccines produced by Pfizer and Moderna is included. We had findings in Summer 2021 similar to those of the Harvard Study done in December 2021, which was based on a half‐million data values, that shows the cost effectiveness of the survey design.
{"title":"New Randomised Response Models for Two Sensitive Characteristics: Theory and Application","authors":"Daryan Naatjes, Stephen A. Sedory, Sarjinder Singh","doi":"10.1111/insr.12555","DOIUrl":"https://doi.org/10.1111/insr.12555","url":null,"abstract":"Summary In this paper, we introduce two new randomised response models for estimating the prevalence of two sensitive characteristics and their overlap in a population by making use of a single deck of cards. The proposed models ensure the privacy of the respondents and also reduce the burden on the respondents as they require the random selection of only one card from a deck of cards each of which contains a pair of questions that are to be answered in order. The variance expressions of the proposed estimators are derived and matched to their Cramer–Rao lower bounds of variances. A simulation study has been carried out to compare the proposed models to each other for least protection. Lastly, a real survey application, related to the acceptability of the vaccines produced by Pfizer and Moderna is included. We had findings in Summer 2021 similar to those of the Harvard Study done in December 2021, which was based on a half‐million data values, that shows the cost effectiveness of the survey design.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135412922","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}
International Statistical ReviewEarly View Book Review Statistical Methods for Climate Scientists Timothy M. DelSole and Michael K. TippettCambridge University Press, 2022, 542 pages, £54.99, hardcover ISBN: 9781108472418 Fabrizio Durante, Corresponding Author Fabrizio Durante [email protected] Dipartimento di Scienze dell'Economia, Università del Salento, Lecce, ItalySearch for more papers by this author Fabrizio Durante, Corresponding Author Fabrizio Durante [email protected] Dipartimento di Scienze dell'Economia, Università del Salento, Lecce, ItalySearch for more papers by this author First published: 20 October 2023 https://doi.org/10.1111/insr.12559Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
《气候科学家的统计方法》Timothy M. DelSole和Michael K. tippette剑桥大学出版社，2022,542页，54.99英镑，精装ISBN:9781108472418法布里齐奥·杜兰特，通讯作者法布里齐奥·杜兰特[email protected]意大利莱切萨伦托大学经济科学博士，通讯作者法布里齐奥·杜兰特[email protected]意大利莱切萨伦托大学经济科学博士，通讯作者法布里齐奥·杜兰特搜索作者更多论文首次发表:2023年10月20日https://doi.org/10.1111/insr.12559Read全文taboutpdf ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare给予accessShare全文accessShare全文accessShare请查看我们的使用条款和条件，并勾选下面的复选框共享文章的全文版本。我已经阅读并接受了Wiley在线图书馆使用共享链接的条款和条件，请使用下面的链接与您的朋友和同事分享本文的全文版本。学习更多的知识。复制URL共享链接共享一个emailfacebooktwitterlinkedinreddit微信本文无摘要在包含问题之前的早期视图在线记录版本相关信息
{"title":"Statistical Methods for Climate ScientistsTimothy M.DelSole and Michael K.TippettCambridge University Press, 2022, 542 pages, £54.99, hardcover ISBN: 9781108472418","authors":"Fabrizio Durante","doi":"10.1111/insr.12559","DOIUrl":"https://doi.org/10.1111/insr.12559","url":null,"abstract":"International Statistical ReviewEarly View Book Review Statistical Methods for Climate Scientists Timothy M. DelSole and Michael K. TippettCambridge University Press, 2022, 542 pages, £54.99, hardcover ISBN: 9781108472418 Fabrizio Durante, Corresponding Author Fabrizio Durante [email protected] Dipartimento di Scienze dell'Economia, Università del Salento, Lecce, ItalySearch for more papers by this author Fabrizio Durante, Corresponding Author Fabrizio Durante [email protected] Dipartimento di Scienze dell'Economia, Università del Salento, Lecce, ItalySearch for more papers by this author First published: 20 October 2023 https://doi.org/10.1111/insr.12559Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135616558","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}
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