International Statistical Review最新文献

{"title":"Likelihood-Based Inference for the Finite Population Mean with Post-Stratification Information Under Non-Ignorable Non-Response","authors":"Sahar Z. Zangeneh,&nbsp;Roderick J. Little","doi":"10.1111/insr.12527","DOIUrl":"10.1111/insr.12527","url":null,"abstract":"<div>\u0000 \u0000 <p>We describe models and likelihood-based estimation of the finite population mean for a survey subject to unit non-response, when post-stratification information is available from external sources. A feature of the models is that they do not require the assumption that the data are missing at random (MAR). As a result, the proposed models provide estimates under weaker assumptions than those required in the absence of post-stratification information, thus allowing more robust inferences. In particular, we describe models for estimation of the finite population mean of a survey outcome with categorical covariates and externally observed categorical post-stratifiers. We compare inferences from the proposed method with existing design-based estimators via simulations. We apply our methods to school-level data from California Department of Education to estimate the mean academic performance index (API) score in years 1999 and 2000. We end with a discussion.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 S1","pages":"S17-S36"},"PeriodicalIF":2.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47286071","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":"Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs","authors":"Elena N. Naumova,&nbsp;Ryan B. Simpson,&nbsp;Bingjie Zhou,&nbsp;Meghan A. Hartwick","doi":"10.1111/insr.12529","DOIUrl":"10.1111/insr.12529","url":null,"abstract":"<div>\u0000 \u0000 <p>The confluence of growing analytic capacities and global surveillance systems for seasonal infections has created new opportunities to further develop statistical methodology and advance the understanding of the global disease dynamics. We developed a framework to characterise the seasonality of infectious diseases for publicly available global health surveillance data. Specifically, we aimed to estimate the seasonal characteristics and their uncertainty using mixed effects models with harmonic components and the δ-method and develop multi-panel visualisations to present complex interplay of seasonal peaks across geographic locations. We compiled a set of 2 422 weekly time series of 14 reported outcomes for 173 Member States from the World Health Organization's (WHO) international influenza virological surveillance system, FluNet, from 02 January 1995 through 20 June 2021. We produced an analecta of data visualisations to describe global travelling waves of influenza while addressing issues of data completeness and credibility. Our results offer directions for further improvements in data collection, reporting, analysis and development of statistical methodology and predictive approaches.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 S1","pages":"S82-S95"},"PeriodicalIF":2.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41356663","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":"Synergy of Biostatistics and Epidemiology in Air Pollution Health Effects Studies","authors":"Douglas W. Dockery","doi":"10.1111/insr.12525","DOIUrl":"10.1111/insr.12525","url":null,"abstract":"<p>The extraordinary advances in quantifying the health effects of ambient air pollution over the last five decades have led to dramatic improvement in air quality in the United States. This work has been possible through innovative epidemiologic study designs coupled with advanced statistical analytic methods. This paper presents a historical perspective on the coordinated developments of epidemiologic designs and statistical methods for air pollution health effects studies at the Harvard School of Public Health.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"90 S1","pages":"S67-S81"},"PeriodicalIF":2.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b1/fc/INSR-90-S67.PMC9828424.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10526357","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":"Path algorithms for fused lasso signal approximator with application to COVID-19 spread in Korea","authors":"Won Son,&nbsp;Johan Lim,&nbsp;Donghyeon Yu","doi":"10.1111/insr.12521","DOIUrl":"10.1111/insr.12521","url":null,"abstract":"<div>\u0000 \u0000 <p>The fused lasso signal approximator (FLSA) is a smoothing procedure for noisy observations that uses fused lasso penalty on unobserved mean levels to find sparse signal blocks. Several path algorithms have been developed to obtain the whole solution path of the FLSA. However, it is known that the FLSA has model selection inconsistency when the underlying signals have a stair-case block, where three consecutive signal blocks are either strictly increasing or decreasing. Modified path algorithms for the FLSA have been proposed to guarantee model selection consistency regardless of the stair-case block. In this paper, we provide a comprehensive review of the path algorithms for the FLSA and prove the properties of the recently modified path algorithms' hitting times. Specifically, we reinterpret the modified path algorithm as the path algorithm for local FLSA problems and reveal the condition that the hitting time for the fusion of the modified path algorithm is not monotone in a tuning parameter. To recover the monotonicity of the solution path, we propose a pathwise adaptive FLSA having monotonicity with similar performance as the modified solution path algorithm. Finally, we apply the proposed method to the number of daily-confirmed cases of COVID-19 in Korea to identify the change points of its spread.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"91 2","pages":"218-242"},"PeriodicalIF":2.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9874640/pdf/INSR-9999-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10584381","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":"Accounting for Non-ignorable Sampling and Non-response in Statistical Matching","authors":"Daniela Marella,&nbsp;Danny Pfeffermann","doi":"10.1111/insr.12524","DOIUrl":"10.1111/insr.12524","url":null,"abstract":"<p>Data for statistical analysis is often available from different samples, with each sample containing measurements on only some of the variables of interest. Statistical matching attempts to generate a fused database containing matched measurements on all the target variables. In this article, we consider the use of statistical matching when the samples are drawn by informative sampling designs and are subject to not missing at random non-response. The problem with ignoring the sampling process and non-response is that the distribution of the data observed for the responding units can be very different from the distribution holding for the population data, which may distort the inference process and result in a matched database that misrepresents the joint distribution in the population. Our proposed methodology employs the empirical likelihood approach and is shown to perform well in a simulation experiment and when applied to real sample data.</p>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"91 2","pages":"269-293"},"PeriodicalIF":2.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/insr.12524","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47607196","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":"Thinking Clearly with Data: A Guide to Quantitative Reasoning and AnalysisEthan BuenodeMesquita and AnthonyFowlerPrinceton University Press, 2021, 400 pages, $95.00/£74.00, hardback ISBN: 978‐0‐691‐21436‐8","authors":"G. Dekkers","doi":"10.1111/insr.12530","DOIUrl":"https://doi.org/10.1111/insr.12530","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49030563","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":"A Bootstrap Variance Procedure for the Generalised Regression Estimator","authors":"Marius Stefan,&nbsp;Michael A. Hidiroglou","doi":"10.1111/insr.12528","DOIUrl":"10.1111/insr.12528","url":null,"abstract":"<div>\u0000 \u0000 <p>The generalised regression estimator (GREG) uses auxiliary data that are available from the finite population to improve the efficiency of the estimator of a total (mean). Estimators of the variance of GREG that have been proposed in the sampling literature include those based on Taylor linearisation and the jackknife techniques. Approximations based on Taylor expansions are reasonable for large samples. However, when the sample size is small, the Taylor-based variance estimator has a large negative bias. The jackknife variance estimators overestimate the variance of GREG for small sample sizes. We offset these setbacks using a bootstrap procedure for estimating the variance of the GREG. The method uses a bootstrap population constructed with the model underlying the GREG estimator. Repeated samples are selected in the bootstrap population according to the design used to select the initial sample, and the variability associated with these bootstrap samples is used to compute the proposed bootstrap variance estimator. Simulations show that the new bootstrap estimator has a small bias for samples that have few observations.</p>\u0000 </div>","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":"91 2","pages":"294-317"},"PeriodicalIF":2.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48498860","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":"Data Visualization for Social and Policy Research: A Step‐by‐Step Approach Using R and PythonJose Manuel MagallanesReyesCambridge University Press, 2022, 292 pages, $105, hardback ISBN: 978‐1‐108‐49433‐5","authors":"Shuangzhe Liu","doi":"10.1111/insr.12531","DOIUrl":"https://doi.org/10.1111/insr.12531","url":null,"abstract":"Producing good visualizations combines creativity and technique. This book teaches the techniques and basics to produce a variety of visualizations, allowing readers to communicate data and analyses in a creative and effective way. Visuals for tables, time series, maps, text, and networks are carefully explained and organized, showing how to choose the right plot for the type of data being analyzed and displayed. Examples are drawn from public policy, public safety, education, political tweets, and public health. The presentation proceeds step by step, starting from the basics, in the programming languages R and Python so that readers learn the coding skills while simultaneously becoming familiar with the advantages and disadvantages of each visualization. No prior knowledge of either Python or R is required. Code for all the visualizations are available from the book’s web site. social science students understand the value of data visualization, but they are wary of the costs of mastering high-tech approaches. Professor Magallanes is the answer to this problem. This text skillfully articulates a step-by-step guide for using two of the most powerful tools in a data scientist’s toolbox: R and Python. Professor Magallanes has a talent for simplifying the complicated, and honing in on the most important components of telling stories with data. This book is an essential resource for anyone whose regular habits of making graphs involve searching for someone else’s code chunks on the Internet. With this book, we can all stop Googling and start graphing.” unique approach of simultaneously introducing users to computational social science programming in both R and Python. The approach just to a language,’ to learn the key conceptual ideas behind programming and computational social science. data collection and statistical analysis, it absolute pleasure the all-important subject of data visualization in this book countless of a second to share the of the matter, imparting the concepts and social science to communicate complex data relationships. the reader through a wide variety of visualization approaches using a conversational style and systematic approach.” copious drawn","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44722958","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 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}

{"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}