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Particle swarm optimization for searching efficient experimental designs: A review 基于粒子群算法的高效实验设计研究进展
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-02-15 DOI: 10.1002/wics.1578
Ping-Yang Chen, Ray‐Bing Chen, W. Wong
{"title":"Particle swarm optimization for searching efficient experimental designs: A review","authors":"Ping-Yang Chen, Ray‐Bing Chen, W. Wong","doi":"10.1002/wics.1578","DOIUrl":"https://doi.org/10.1002/wics.1578","url":null,"abstract":"The class of nature‐inspired metaheuristic algorithms is increasingly used to tackle all kinds of optimization problems across disciplines. It also plays an important component in artificial intelligence and machine learning. Members in this class are general purpose optimization tools that virtually require no assumptions for them to be applicable. There are many such algorithms, and to fix ideas, we review one of its exemplary members called particle swarm optimization (PSO). We discuss the algorithm, its recent applications to find different types of efficient experimental designs, and provide resources, where codes for PSO and other metaheuristic algorithms and tutorials with examples are available.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44544982","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: 11
Issue Information 问题信息
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-01-01 DOI: 10.1002/wics.1560
{"title":"Issue Information","authors":"","doi":"10.1002/wics.1560","DOIUrl":"https://doi.org/10.1002/wics.1560","url":null,"abstract":"","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":"14 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41634536","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
SAS 特种空军部队
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2022-01-01 DOI: 10.1002/wics.131
Robert N. Rodriguez
{"title":"SAS","authors":"Robert N. Rodriguez","doi":"10.1002/wics.131","DOIUrl":"https://doi.org/10.1002/wics.131","url":null,"abstract":"SAS® software is a comprehensive set of integrated tools and solutions for accessing, managing, and analyzing data. SAS, which was formed as a company in 1976, is a leading developer of statistical software, which is widely used in academic, business, and government organizations. Since the 1980s, SAS has expanded its analytical software to include forecasting and econometrics, data mining, text mining, and operations research. SAS now builds on these components to provide software for business analytics and solutions for industry‐specific problems such as customer intelligence, fraud prevention, and risk management. This article describes the evolution of SAS as a company and overviews new directions in its analytical software. An example program illustrates key elements of SAS programming that are useful for statistical analysis. WIREs Comp Stat 2011 3 1–11 DOI: 10.1002/wics.131","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":"3 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51212570","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
Pharmacokinetics 药代动力学
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2021-12-17 DOI: 10.1002/wics.153
P. Bonate
{"title":"Pharmacokinetics","authors":"P. Bonate","doi":"10.1002/wics.153","DOIUrl":"https://doi.org/10.1002/wics.153","url":null,"abstract":"Pharmacokinetics is the study of the absorption, distribution, metabolism, and excretion of drugs. Simply put, pharmacokinetics is what the body does to the drug, which is opposed to pharmacodynamics, which is what the drug does to the body. This review will introduce pharmacokinetics as a science showing how biochemistry, biology, pharmacology, and physiology interact to explain how a drug ‘works’. Pharmacokinetics is becoming increasingly quantitative with population pharmacokinetics–pharmacodynamics, which uses nonlinear mixed effects model methodology, becoming more and more commonplace and acting as the core to model‐based drug development. This review will show how statistics is becoming increasingly more important in pharmacokinetic analysis and provide an introduction to statistical analysis of pharmacokinetic data. WIREs Comp Stat 2011 3 332–342 DOI: 10.1002/wics.153","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":"3 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42324847","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
The state‐of‐the‐art on tours for dynamic visualization of high‐dimensional data 最先进的高维数据动态可视化漫游技术
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2021-12-09 DOI: 10.1002/wics.1573
Stuart Lee, Dianne Cook, N. Silva, U. Laa, N. Spyrison, Earo Wang, H. S. Zhang
{"title":"The state‐of‐the‐art on tours for dynamic visualization of high‐dimensional data","authors":"Stuart Lee, Dianne Cook, N. Silva, U. Laa, N. Spyrison, Earo Wang, H. S. Zhang","doi":"10.1002/wics.1573","DOIUrl":"https://doi.org/10.1002/wics.1573","url":null,"abstract":"This article discusses a high‐dimensional visualization technique called the tour, which can be used to view data in more than three dimensions. We review the theory and history behind the technique, as well as modern software developments and applications of the tour that are being found across the sciences and machine learning.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46278789","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: 4
Nearest‐neighbor sparse Cholesky matrices in spatial statistics 空间统计学中的最近邻稀疏Cholesky矩阵
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2021-12-08 DOI: 10.1002/wics.1574
A. Datta
{"title":"Nearest‐neighbor sparse Cholesky matrices in spatial statistics","authors":"A. Datta","doi":"10.1002/wics.1574","DOIUrl":"https://doi.org/10.1002/wics.1574","url":null,"abstract":"Gaussian process (GP) is a staple in the toolkit of a spatial statistician. Well‐documented computing roadblocks in the analysis of large geospatial datasets using GPs have now largely been mitigated via several recent statistical innovations. Nearest neighbor Gaussian process (NNGP) has emerged as one of the leading candidates for such massive‐scale geospatial analysis owing to their empirical success. This article reviews the connection of NNGP to sparse Cholesky factors of the spatial precision (inverse‐covariance) matrix. Focus of the review is on these sparse Cholesky matrices which are versatile and have recently found many diverse applications beyond the primary usage of NNGP for fast parameter estimation and prediction in the spatial (generalized) linear models. In particular, we discuss applications of sparse NNGP Cholesky matrices to address multifaceted computational issues in spatial bootstrapping, simulation of large‐scale realizations of Gaussian random fields, and extensions to nonparametric mean function estimation of a GP using random forests. We also review a sparse‐Cholesky‐based model for areal (geographically aggregated) data that addresses long‐established interpretability issues of existing areal models. Finally, we highlight some yet‐to‐be‐addressed issues of such sparse Cholesky approximations that warrant further research.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49411661","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: 7
The how and why of Bayesian nonparametric causal inference 贝叶斯非参数因果推理的方法和原因
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2021-11-06 DOI: 10.1002/wics.1583
A. Linero, Joseph Antonelli
{"title":"The how and why of Bayesian nonparametric causal inference","authors":"A. Linero, Joseph Antonelli","doi":"10.1002/wics.1583","DOIUrl":"https://doi.org/10.1002/wics.1583","url":null,"abstract":"Spurred on by recent successes in causal inference competitions, Bayesian nonparametric (and high‐dimensional) methods have recently seen increased attention in the causal inference literature. In this article, we present a comprehensive overview of Bayesian nonparametric applications to causal inference. Our aims are to (i) introduce the fundamental Bayesian nonparametric toolkit; (ii) discuss how to determine which tool is most appropriate for a given problem; and (iii) show how to avoid common pitfalls in applying Bayesian nonparametric methods in high‐dimensional settings. Unlike standard fixed‐dimensional parametric problems, where outcome modeling alone can sometimes be effective, we argue that most of the time it is necessary to model both the selection and outcome processes.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44081453","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: 9
Joint Gaussian graphical model estimation: A survey 联合高斯图形模型估计:综述
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2021-10-19 DOI: 10.1002/wics.1582
Katherine Tsai, Oluwasanmi Koyejo, M. Kolar
{"title":"Joint Gaussian graphical model estimation: A survey","authors":"Katherine Tsai, Oluwasanmi Koyejo, M. Kolar","doi":"10.1002/wics.1582","DOIUrl":"https://doi.org/10.1002/wics.1582","url":null,"abstract":"Graphs representing complex systems often share a partial underlying structure across domains while retaining individual features. Thus, identifying common structures can shed light on the underlying signal, for instance, when applied to scientific discovery or clinical diagnoses. Furthermore, growing evidence shows that the shared structure across domains boosts the estimation power of graphs, particularly for high‐dimensional data. However, building a joint estimator to extract the common structure may be more complicated than it seems, most often due to data heterogeneity across sources. This manuscript surveys recent work on statistical inference of joint Gaussian graphical models, identifying model structures that fit various data generation processes.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48214868","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: 8
Issue Information 问题信息
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2021-10-14 DOI: 10.1002/wics.1522
{"title":"Issue Information","authors":"","doi":"10.1002/wics.1522","DOIUrl":"https://doi.org/10.1002/wics.1522","url":null,"abstract":"","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49277217","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
Cluster‐scaled principal component analysis 聚类尺度主成分分析
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2021-10-02 DOI: 10.1002/wics.1572
M. Sato-Ilic
{"title":"Cluster‐scaled principal component analysis","authors":"M. Sato-Ilic","doi":"10.1002/wics.1572","DOIUrl":"https://doi.org/10.1002/wics.1572","url":null,"abstract":"Cluster‐scaled analysis means exploiting the cluster‐based scaling to conventional data analysis to obtain more accurate results or results that we cannot obtain by using ordinary analysis. Our target data is complex and large amounts of data. For this type of data, it is well known that ordinary statistical methods do not always work well, or theoretically, we know that we cannot obtain a correct result. As a tool of this implementation, we utilize fuzzy clustering, which is well known as a robust clustering to a complex and large amount of data. That is, we use the fuzzy clustering result as a scale of data and apply the rescaled data by the cluster‐scale to another target analysis. Our target analysis in this article is principal component analysis, which is a well‐known dimensional reduction method. A numerical example shows a better performance of the cluster‐scaled principal component analysis.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48365950","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: 3
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