{"title":"Statistics: Multivariate Data Integration Using R; Methods and Applications With the mixOmics Package Kim-Anh Lê Cao, Zoe Marie WelhamChapman & Hall/CRC, 2021, xxi + 308 pages, £84.99/$115.00, hardcover ISBN: 978-1032128078 eBook ISBN: 9781003026860","authors":"Krzysztof Podgórski","doi":"10.1111/insr.12599","DOIUrl":"https://doi.org/10.1111/insr.12599","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579677","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":"Machine Learning Theory and Applications: Hands-On Use Cases With Python on Classical and Quantum Machines, Xavier Vasques, John Wiley & Sons, 2024, xx + 487 pages, $89.95, hardcover ISBN: 978-1-394-22061-8","authors":"Shuangzhe Liu","doi":"10.1111/insr.12602","DOIUrl":"https://doi.org/10.1111/insr.12602","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579757","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":"Object Oriented Data Analysis J. S. Marron and I. L. DrydenChapman & Hall/CRC, 2022, xii + 424 pages, softcover ISBN: 978-0-8153-9282-8 (hbk) ISBN: 978-1-032-11480-4 (pbk) ISBN: 978-1-351-18967-5 (ebk)","authors":"Debashis Ghosh","doi":"10.1111/insr.12600","DOIUrl":"https://doi.org/10.1111/insr.12600","url":null,"abstract":"","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579760","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}
Roberto Benedetti, Federica Piersimoni, Monica Pratesi, Nicola Salvati, Thomas Suesse
SummaryUnemployment rate estimates for small areas are used to efficiently support the distribution of services and the allocation of resources, grants and funding. A Fay–Herriot type model is the most used tool to obtain these estimates. Under this approach out‐of‐sample areas require some synthetic estimates. As the geographical context is extremely important for analysing local economies, in this paper, we allow for area random effects to be spatially correlated. The spatial model parameters are estimated by a marginal likelihood method and are used to predict in‐sample as well as out‐of‐sample areas. Extensive simulation experiments are used to assess the impact of the auto‐regression parameter and of the rate of out‐of‐sample areas on the performance of this approach. The paper concludes with an illustrative application on real data from the Italian Labour Force Survey in which the estimation of the unemployment rate in each Local Labour Market Area is addressed.
{"title":"Handling Out‐of‐Sample Areas to Estimate the Unemployment Rate at Local Labour Market Areas in Italy","authors":"Roberto Benedetti, Federica Piersimoni, Monica Pratesi, Nicola Salvati, Thomas Suesse","doi":"10.1111/insr.12596","DOIUrl":"https://doi.org/10.1111/insr.12596","url":null,"abstract":"SummaryUnemployment rate estimates for small areas are used to efficiently support the distribution of services and the allocation of resources, grants and funding. A Fay–Herriot type model is the most used tool to obtain these estimates. Under this approach out‐of‐sample areas require some synthetic estimates. As the geographical context is extremely important for analysing local economies, in this paper, we allow for area random effects to be spatially correlated. The spatial model parameters are estimated by a marginal likelihood method and are used to predict in‐sample as well as out‐of‐sample areas. Extensive simulation experiments are used to assess the impact of the auto‐regression parameter and of the rate of out‐of‐sample areas on the performance of this approach. The paper concludes with an illustrative application on real data from the Italian Labour Force Survey in which the estimation of the unemployment rate in each Local Labour Market Area is addressed.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182405","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}
Tuo Lin, Ruohui Chen, Jinyuan Liu, Tsungchin Wu, Toni T. Gui, Yangyi Li, Xinyi Huang, Kun Yang, Guanqing Chen, Tian Chen, David R. Strong, Karen Messer, Xin M. Tu
SummaryProbability weights have been widely used in addressing selection bias arising from a variety of contexts. Common examples of probability weights include sampling weights, missing data weights, and propensity score weights. Frequency weights, which are used to control for varying variabilities of aggregated outcomes, are both conceptually and analytically different from probability weights. Popular software such as R, SAS and STATA support both types of weights. Many users, including professional statisticians, become bewildered when they see identical estimates, but different standard errors and ‐values when probability weights are treated as frequency weights. Some even completely ignore the difference between the two types of weights and treat them as the same. Although a large body of literature exists on each type of weights, we have found little, if any, discussion that provides head‐to‐head comparisons of the two types of weights and associated inference methods. In this paper, we unveil the conceptual and analytic differences between the two types of weights within the context of parametric and semi‐parametric generalised linear models (GLM) and discuss valid inference for each type of weights. To the best of our knowledge, this is the first paper that looks into such differences by identifying the conditions under which the two types of weights can be treated the same analytically and providing clear guidance on the appropriate statistical models and inference procedures for each type of weights. We illustrate these considerations using real study data.
摘要概率权重已被广泛用于解决各种情况下产生的选择偏差。概率权重的常见例子包括抽样权重、缺失数据权重和倾向得分权重。频率权重用于控制汇总结果的不同变异性，在概念和分析上都不同于概率权重。R、SAS 和 STATA 等流行软件都支持这两种类型的权重。当包括专业统计人员在内的许多用户看到相同的估计值，但当概率权重被视为频率权重时，却有不同的标准误和-值时，他们会感到困惑。有些人甚至完全忽略了这两种权重的区别，将它们视为相同的权重。尽管存在大量关于每种权重类型的文献，但我们几乎没有发现对这两种权重类型和相关推断方法进行正面比较的讨论。在本文中，我们将揭示参数和半参数广义线性模型（GLM）中两种权重在概念和分析上的差异，并讨论每种权重的有效推断方法。据我们所知，这是第一篇研究这种差异的论文，它确定了在哪些条件下可以对两类权重进行相同的分析处理，并就每类权重的适当统计模型和推断程序提供了明确的指导。我们使用实际研究数据来说明这些考虑因素。
{"title":"On Frequency and Probability Weights: An In‐Depth Look at Duelling Weights","authors":"Tuo Lin, Ruohui Chen, Jinyuan Liu, Tsungchin Wu, Toni T. Gui, Yangyi Li, Xinyi Huang, Kun Yang, Guanqing Chen, Tian Chen, David R. Strong, Karen Messer, Xin M. Tu","doi":"10.1111/insr.12594","DOIUrl":"https://doi.org/10.1111/insr.12594","url":null,"abstract":"SummaryProbability weights have been widely used in addressing selection bias arising from a variety of contexts. Common examples of probability weights include sampling weights, missing data weights, and propensity score weights. Frequency weights, which are used to control for varying variabilities of aggregated outcomes, are both conceptually and analytically different from probability weights. Popular software such as R, SAS and STATA support both types of weights. Many users, including professional statisticians, become bewildered when they see identical estimates, but different standard errors and ‐values when probability weights are treated as frequency weights. Some even completely ignore the difference between the two types of weights and treat them as the same. Although a large body of literature exists on each type of weights, we have found little, if any, discussion that provides head‐to‐head comparisons of the two types of weights and associated inference methods. In this paper, we unveil the conceptual and analytic differences between the two types of weights within the context of parametric and semi‐parametric generalised linear models (GLM) and discuss valid inference for each type of weights. To the best of our knowledge, this is the first paper that looks into such differences by identifying the conditions under which the two types of weights can be treated the same analytically and providing clear guidance on the appropriate statistical models and inference procedures for each type of weights. We illustrate these considerations using real study data.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182486","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}
SummaryClustering of longitudinal data is becoming increasingly popular in many fields such as social sciences, business, environmental science, medicine and healthcare. However, it is often challenging due to the complex nature of the data, such as dependencies between observations collected over time, missingness, sparsity and non‐linearity, making it difficult to identify meaningful patterns and relationships among the data. Despite the increasingly common application of cluster analysis for longitudinal data, many existing methods are still less known to researchers, and limited guidance is provided in choosing between methods and software packages. In this paper, we review several commonly used methods for clustering longitudinal data. These methods are broadly classified into three categories, namely, model‐based approaches, algorithm‐based approaches and functional clustering approaches. We perform a comparison among these methods and their corresponding R software packages using real‐life datasets and simulated datasets under various conditions. Findings from the analyses and recommendations for using these approaches in practice are discussed.
摘要 纵向数据聚类在社会科学、商业、环境科学、医学和医疗保健等许多领域越来越受欢迎。然而，由于数据的复杂性，如随着时间推移收集到的观测数据之间的依赖性、缺失性、稀疏性和非线性，使得识别数据之间有意义的模式和关系变得十分困难。尽管聚类分析在纵向数据中的应用越来越普遍，但研究人员对许多现有方法的了解仍然较少，在选择方法和软件包方面提供的指导也很有限。在本文中，我们回顾了几种常用的纵向数据聚类方法。这些方法大致分为三类，即基于模型的方法、基于算法的方法和功能聚类方法。我们使用真实数据集和各种条件下的模拟数据集对这些方法及其相应的 R 软件包进行了比较。我们讨论了分析结果以及在实践中使用这些方法的建议。
{"title":"Clustering Longitudinal Data: A Review of Methods and Software Packages","authors":"Zihang Lu","doi":"10.1111/insr.12588","DOIUrl":"https://doi.org/10.1111/insr.12588","url":null,"abstract":"SummaryClustering of longitudinal data is becoming increasingly popular in many fields such as social sciences, business, environmental science, medicine and healthcare. However, it is often challenging due to the complex nature of the data, such as dependencies between observations collected over time, missingness, sparsity and non‐linearity, making it difficult to identify meaningful patterns and relationships among the data. Despite the increasingly common application of cluster analysis for longitudinal data, many existing methods are still less known to researchers, and limited guidance is provided in choosing between methods and software packages. In this paper, we review several commonly used methods for clustering longitudinal data. These methods are broadly classified into three categories, namely, model‐based approaches, algorithm‐based approaches and functional clustering approaches. We perform a comparison among these methods and their corresponding R software packages using real‐life datasets and simulated datasets under various conditions. Findings from the analyses and recommendations for using these approaches in practice are discussed.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182504","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}
SummaryAmong the variety of statistical intervals, highest‐density regions (HDRs) stand out for their ability to effectively summarise a distribution or sample, unveiling its distinctive and salient features. An HDR represents the minimum size set that satisfies a certain probability coverage, and current methods for their computation require knowledge or estimation of the underlying probability distribution or density . In this work, we illustrate a broader framework for computing HDRs, which generalises the classical density quantile method. The framework is based on neighbourhood measures, that is, measures that preserve the order induced in the sample by , and include the density as a special case. We explore a number of suitable distance‐based measures, such as the ‐nearest neighbourhood distance, and some probabilistic variants based on copula models. An extensive comparison is provided, showing the advantages of the copula‐based strategy, especially in those scenarios that exhibit complex structures (e.g. multimodalities or particular dependencies). Finally, we discuss the practical implications of our findings for estimating HDRs in real‐world applications.
{"title":"Alternative Approaches for Estimating Highest‐Density Regions","authors":"Nina Deliu, Brunero Liseo","doi":"10.1111/insr.12592","DOIUrl":"https://doi.org/10.1111/insr.12592","url":null,"abstract":"SummaryAmong the variety of statistical intervals, highest‐density regions (HDRs) stand out for their ability to effectively summarise a distribution or sample, unveiling its distinctive and salient features. An HDR represents the minimum size set that satisfies a certain probability coverage, and current methods for their computation require knowledge or estimation of the underlying probability distribution or density . In this work, we illustrate a broader framework for computing HDRs, which generalises the classical density quantile method. The framework is based on <jats:italic>neighbourhood</jats:italic> measures, that is, measures that preserve the order induced in the sample by , and include the density as a special case. We explore a number of suitable distance‐based measures, such as the ‐nearest neighbourhood distance, and some probabilistic variants based on <jats:italic>copula models</jats:italic>. An extensive comparison is provided, showing the advantages of the copula‐based strategy, especially in those scenarios that exhibit complex structures (e.g. multimodalities or particular dependencies). Finally, we discuss the practical implications of our findings for estimating HDRs in real‐world applications.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182503","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}
SummaryThe mixture models are widely used to analyze data with cluster structures and the mixture of Gaussians is most common in practical applications. The use of mixtures involving other multivariate distributions, like the multivariate skew normal and multivariate generalised hyperbolic, is also found in the literature. However, in all such cases, only the mixtures of identical distributions are used to form a mixture model. We present an innovative and versatile approach for constructing mixture models involving identical and non‐identical distributions combined in all conceivable permutations (e.g. a mixture of multivariate skew normal and multivariate generalised hyperbolic). We also establish any conventional mixture model as a distinctive particular case of our proposed framework. The practical efficacy of our model is shown through its application to both simulated and real‐world data sets. Our comprehensive and flexible model excels at recognising inherent patterns and accurately estimating parameters.
{"title":"Flexible Multivariate Mixture Models: A Comprehensive Approach for Modeling Mixtures of Non‐Identical Distributions","authors":"Samyajoy Pal, Christian Heumann","doi":"10.1111/insr.12593","DOIUrl":"https://doi.org/10.1111/insr.12593","url":null,"abstract":"SummaryThe mixture models are widely used to analyze data with cluster structures and the mixture of Gaussians is most common in practical applications. The use of mixtures involving other multivariate distributions, like the multivariate skew normal and multivariate generalised hyperbolic, is also found in the literature. However, in all such cases, only the mixtures of identical distributions are used to form a mixture model. We present an innovative and versatile approach for constructing mixture models involving identical and non‐identical distributions combined in all conceivable permutations (e.g. a mixture of multivariate skew normal and multivariate generalised hyperbolic). We also establish any conventional mixture model as a distinctive particular case of our proposed framework. The practical efficacy of our model is shown through its application to both simulated and real‐world data sets. Our comprehensive and flexible model excels at recognising inherent patterns and accurately estimating parameters.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933219","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}
Zeyi Wang, Eric Bridgeford, Shangsi Wang, Joshua T. Vogelstein, Brian Caffo
SummaryThe advent of modern data collection and processing techniques has seen the size, scale and complexity of data grow exponentially. A seminal step in leveraging these rich datasets for downstream inference is understanding the characteristics of the data which are repeatable—the aspects of the data that are able to be identified under duplicated analyses. Conflictingly, the utility of traditional repeatability measures, such as the intra‐class correlation coefficient, under these settings is limited. In recent work, novel data repeatability measures have been introduced in the context where a set of subjects are measured twice or more, including: fingerprinting, rank sums and generalisations of the intra‐class correlation coefficient. However, the relationships between, and the best practices among, these measures remains largely unknown. In this manuscript, we formalise a novel repeatability measure, discriminability. We show that it is deterministically linked with the intra‐class correlation coefficients under univariate random effect models and has the desired property of optimal accuracy for inferential tasks using multivariate measurements. Additionally, we overview and systematically compare existing repeatability statistics with discriminability, using both theoretical results and simulations. We show that the rank sum statistic is deterministically linked to a consistent estimator of discriminability. The statistical power of permutation tests derived from these measures are compared numerically under Gaussian and non‐Gaussian settings, with and without simulated batch effects. Motivated by both theoretical and empirical results, we provide methodological recommendations for each benchmark setting to serve as a resource for future analyses. We believe these recommendations will play an important role towards improving repeatability in fields such as functional magnetic resonance imaging, genomics, pharmacology and more.
{"title":"Statistical Analysis of Data Repeatability Measures","authors":"Zeyi Wang, Eric Bridgeford, Shangsi Wang, Joshua T. Vogelstein, Brian Caffo","doi":"10.1111/insr.12591","DOIUrl":"https://doi.org/10.1111/insr.12591","url":null,"abstract":"SummaryThe advent of modern data collection and processing techniques has seen the size, scale and complexity of data grow exponentially. A seminal step in leveraging these rich datasets for downstream inference is understanding the characteristics of the data which are repeatable—the aspects of the data that are able to be identified under duplicated analyses. Conflictingly, the utility of traditional repeatability measures, such as the intra‐class correlation coefficient, under these settings is limited. In recent work, novel data repeatability measures have been introduced in the context where a set of subjects are measured twice or more, including: fingerprinting, rank sums and generalisations of the intra‐class correlation coefficient. However, the relationships between, and the best practices among, these measures remains largely unknown. In this manuscript, we formalise a novel repeatability measure, discriminability. We show that it is deterministically linked with the intra‐class correlation coefficients under univariate random effect models and has the desired property of optimal accuracy for inferential tasks using multivariate measurements. Additionally, we overview and systematically compare existing repeatability statistics with discriminability, using both theoretical results and simulations. We show that the rank sum statistic is deterministically linked to a consistent estimator of discriminability. The statistical power of permutation tests derived from these measures are compared numerically under Gaussian and non‐Gaussian settings, with and without simulated batch effects. Motivated by both theoretical and empirical results, we provide methodological recommendations for each benchmark setting to serve as a resource for future analyses. We believe these recommendations will play an important role towards improving repeatability in fields such as functional magnetic resonance imaging, genomics, pharmacology and more.","PeriodicalId":14479,"journal":{"name":"International Statistical Review","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933273","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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