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.
{"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}
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
SAS®软件是一套全面的集成工具和解决方案，用于访问、管理和分析数据。SAS成立于1976年，是一家领先的统计软件开发商，广泛应用于学术、商业和政府组织。自20世纪80年代以来，SAS已经扩展了其分析软件，包括预测和计量经济学、数据挖掘、文本挖掘和运筹学。SAS现在以这些组件为基础，为客户情报、欺诈预防和风险管理等行业特定问题提供业务分析软件和解决方案。本文描述了SAS作为一家公司的演变，并概述了其分析软件的新方向。一个示例程序说明了SAS编程中对统计分析有用的关键元素。WIREs Comp Stat 2011 31 - 11 DOI: 10.1002/wics.131
{"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}
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
药代动力学是研究药物的吸收、分布、代谢和排泄。简单地说，药代动力学是身体对药物的作用，而药效学是药物对身体的作用。这篇综述将介绍药代动力学作为一门科学，展示生物化学、生物学、药理学和生理学如何相互作用，以解释药物是如何“工作”的。药物动力学正变得越来越定量，群体药物动力学-药效学使用非线性混合效应模型方法，变得越来越普遍，并成为基于模型的药物开发的核心。这篇综述将展示统计学在药代动力学分析中的重要性，并介绍药代动力学数据的统计分析。WIREs Comp Stat 2011 3 332–342 DOI:10.1002/wics.153
{"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}
Stuart Lee, Dianne Cook, N. Silva, U. Laa, N. Spyrison, Earo Wang, H. S. Zhang
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.
{"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}
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.
{"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}
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.
{"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}
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.
{"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}
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.
{"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}
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