Wiley Interdisciplinary Reviews-Computational Statistics最新文献_第2页

Stability estimation for unsupervised clustering: A review. 无监督聚类的稳定性估计：综述。

IF 4.4 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-11-01 Epub Date: 2022-01-09 DOI: 10.1002/wics.1575

Tianmou Liu, Han Yu, Rachael Hageman Blair

{"title":"Stability estimation for unsupervised clustering: A review.","authors":"Tianmou Liu, Han Yu, Rachael Hageman Blair","doi":"10.1002/wics.1575","DOIUrl":"10.1002/wics.1575","url":null,"abstract":"Cluster analysis remains one of the most challenging yet fundamental tasks in unsupervised learning. This is due in part to the fact that there are no labels or gold standards by which performance can be measured. Moreover, the wide range of clustering methods available is governed by different objective functions, different parameters, and dissimilarity measures. The purpose of clustering is versatile, often playing critical roles in the early stages of exploratory data analysis and as an endpoint for knowledge and discovery. Thus, understanding the quality of a clustering is of critical importance. The concept of stability has emerged as a strategy for assessing the performance and reproducibility of data clustering. The key idea is to produce perturbed data sets that are very close to the original, and cluster them. If the clustering is stable, then the clusters from the original data will be preserved in the perturbed data clustering. The nature of the perturbation, and the methods for quantifying similarity between clusterings, are nontrivial, and ultimately what distinguishes many of the stability estimation methods apart. In this review, we provide an overview of the very active research area of cluster stability estimation and discuss some of the open questions and challenges that remain in the field. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/0e/84/WICS-14-e1575.PMC9787023.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10512933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey of numerical algorithms that can solve the Lasso problems 求解Lasso问题的数值算法综述

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-10-24 DOI: 10.1002/wics.1602

Yujie Zhao, X. Huo

引用次数: 4

Data validation and statistical issues such as power and other considerations in genome‐wide association study (GWAS) 数据验证和统计问题，如全基因组关联研究(GWAS)中的功率和其他考虑因素

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-10-06 DOI: 10.1002/wics.1601

Makoto Tomita

引用次数: 0

On unbiasedness and biasedness of the Wilcoxon and some nonparametric tests 关于Wilcoxon的无偏性和偏性及一些非参数检验

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-09-23 DOI: 10.1002/wics.1600

H. Murakami, Seong-Keon Lee

{"title":"On unbiasedness and biasedness of the Wilcoxon and some nonparametric tests","authors":"H. Murakami, Seong-Keon Lee","doi":"10.1002/wics.1600","DOIUrl":"https://doi.org/10.1002/wics.1600","url":null,"abstract":"In several fields of applications, the underlying theoretical distribution is unknown and cannot be assumed to have a specific parametric distribution such as a normal distribution. Nonparametric statistical methods are preferable in these cases. Nonparametric testing hypotheses have been one of the primarily used statistical procedures for nearly a century, and the power of the test is an important property in nonparametric testing procedures. This review discusses the unbiasedness of nonparametric tests. In nonparametric hypothesis, the best‐known Wilcoxon–Mann–Whitney (WMW) test has both robustness and power performance. Therefore, the WMW test is widely used to determine the location parameter. In this review, the unbiasedness and biasedness of the WMW test for the location parameter family of the distribution is mainly investigated. An overview of historical developments, detailed discussions, and works on the unbiasedness/biasedness of several nonparametric tests are presented with references to numerous studies. Finally, we conclude this review with a discussion on the unbiasedness/biasedness of nonparametric test procedures. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Nonparametric Methods.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48942026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

A review of recent advances in empirical likelihood 对经验可能性的最新进展的回顾

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-09-20 DOI: 10.1002/wics.1599

Pang-Chi Liu, Yichuan Zhao

引用次数: 5

Sequential Monte Carlo optimization and statistical inference 顺序蒙特卡罗优化和统计推断

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-09-20 DOI: 10.1002/wics.1598

J. Duan, Shuping Li, Yaxian Xu

{"title":"Sequential Monte Carlo optimization and statistical inference","authors":"J. Duan, Shuping Li, Yaxian Xu","doi":"10.1002/wics.1598","DOIUrl":"https://doi.org/10.1002/wics.1598","url":null,"abstract":"Sequential Monte Carlo (SMC) is a powerful technique originally developed for particle filtering and Bayesian inference. As a generic optimizer for statistical and nonstatistical objectives, its role is far less known. Density‐tempered SMC is a highly efficient sampling technique ideally suited for challenging global optimization problems and is implementable with a somewhat arbitrary initialization sampler instead of relying on a prior distribution. SMC optimization is anchored at the fact that all optimization tasks (continuous, discontinuous, combinatorial, or noisy objective function) can be turned into sampling under a density or probability function short of a norming constant. The point with the highest functional value is the SMC estimate for the maximum. Through examples, we systematically present various density‐tempered SMC algorithms and their superior performance vs. other techniques like Markov Chain Monte Carlo. Data cloning and k‐fold duplication are two easily implementable accuracy accelerators, and their complementarity is discussed. The Extreme Value Theorem on the maximum order statistic can also help assess the quality of the SMC optimum. Our coverage includes the algorithmic essence of the density‐tempered SMC with various enhancements and solutions for (1) a bi‐modal nonstatistical function without and with constraints, (2) a multidimensional step function, (3) offline and online optimizations, (4) combinatorial variable selection, and (5) noninvertibility of the Hessian.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42620366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Issue Information 问题信息

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-09-01 DOI: 10.1111/papq.12360

引用次数: 0

Cluster analysis: A modern statistical review 聚类分析:现代统计回顾

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-08-19 DOI: 10.1002/wics.1597

Adam Jaeger, David Banks

引用次数: 8

Robust regression using probabilistically linked data 使用概率关联数据的稳健回归

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-07-07 DOI: 10.1002/wics.1596

R. Chambers, E. Fabrizi, M. Ranalli, N. Salvati, Suojin Wang

{"title":"Robust regression using probabilistically linked data","authors":"R. Chambers, E. Fabrizi, M. Ranalli, N. Salvati, Suojin Wang","doi":"10.1002/wics.1596","DOIUrl":"https://doi.org/10.1002/wics.1596","url":null,"abstract":"There is growing interest in a data integration approach to survey sampling, particularly where population registers are linked for sampling and subsequent analysis. The reason for doing this is simple: it is only by linking the same individuals in the different sources that it becomes possible to create a data set suitable for analysis. But data linkage is not error free. Many linkages are nondeterministic, based on how likely a linking decision corresponds to a correct match, that is, it brings together the same individual in all sources. High quality linking will ensure that the probability of this happening is high. Analysis of the linked data should take account of this additional source of error when this is not the case. This is especially true for secondary analysis carried out without access to the linking information, that is, the often confidential data that agencies use in their record matching. We describe an inferential framework that allows for linkage errors when sampling from linked registers. After first reviewing current research activity in this area, we focus on secondary analysis and linear regression modeling, including the important special case of estimation of subpopulation and small area means. In doing so we consider both robustness and efficiency of the resulting linked data inferences.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2022-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46408778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

A review of second-order blind identification methods. 二阶盲识别方法综述。

IF 4.4 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-07-01 Epub Date: 2021-02-07 DOI: 10.1002/wics.1550

Yan Pan, Markus Matilainen, Sara Taskinen, Klaus Nordhausen

{"title":"A review of second-order blind identification methods.","authors":"Yan Pan, Markus Matilainen, Sara Taskinen, Klaus Nordhausen","doi":"10.1002/wics.1550","DOIUrl":"10.1002/wics.1550","url":null,"abstract":"Second-order source separation (SOS) is a data analysis tool which can be used for revealing hidden structures in multivariate time series data or as a tool for dimension reduction. Such methods are nowadays increasingly important as more and more high-dimensional multivariate time series data are measured in numerous fields of applied science. Dimension reduction is crucial, as modeling such high-dimensional data with multivariate time series models is often impractical as the number of parameters describing dependencies between the component time series is usually too high. SOS methods have their roots in the signal processing literature, where they were first used to separate source signals from an observed signal mixture. The SOS model assumes that the observed time series (signals) is a linear mixture of latent time series (sources) with uncorrelated components. The methods make use of the second-order statistics-hence the name \"second-order source separation.\" In this review, we discuss the classical SOS methods and their extensions to more complex settings. An example illustrates how SOS can be performed. This article is categorized under:Statistical Models > Time Series ModelsStatistical and Graphical Methods of Data Analysis > Dimension ReductionData: Types and Structure > Time Series, Stochastic Processes, and Functional Data.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9540980/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33518523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0