International Statistical Review最新文献

{"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}

{"title":"Geographic Data Science With Python Sergio Rey, Dani Arribas-Bel, Levi John WolfCRC Press, 2023, xiv +  410 pages, 119 Color & 4 B/W Illustrations, £39.19/$89.95, paperback ISBN: 978-1-032-44595-3 (pbk)","authors":"Krzysztof Podgórski","doi":"10.1111/insr.12569","DOIUrl":"10.1111/insr.12569","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140004792","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}

{"title":"Maximum Likelihood Estimation of Multivariate Regime Switching Student-t Copula Models","authors":"Federico P. Cortese,&nbsp;Fulvia Pennoni,&nbsp;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}

{"title":"Parametric Quantile Beta Regression Model","authors":"Marcelo Bourguignon,&nbsp;Diego I. Gallardo,&nbsp;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}

{"title":"On the Inversion-Free Newton's Method and Its Applications","authors":"Huy N. Chau,&nbsp;J. Lars Kirkby,&nbsp;Dang H. Nguyen,&nbsp;Duy Nguyen,&nbsp;Nhu N. Nguyen,&nbsp;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}

{"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}

{"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}

{"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}

{"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}

{"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}