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Two Tales Of Variable Selection For High Dimensional Data
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Book Synopsis Multiple Classifier Systems by : Friedhelm Schwenker
Download or read book Multiple Classifier Systems written by Friedhelm Schwenker and published by Springer. This book was released on 2015-06-02 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th International Workshop on Multiple Classifier Systems, MCS 2015, held in Günzburg, Germany, in June/July 2015. The 19 revised papers presented were carefully reviewed and selected from 25 submissions. The papers address issues in multiple classifier systems and ensemble methods, including pattern recognition, machine learning, neural network, data mining and statistics. They are organized in topical sections on theory and algorithms and application and evaluation.
Book Synopsis Statistical Learning with Sparsity by : Trevor Hastie
Download or read book Statistical Learning with Sparsity written by Trevor Hastie and published by CRC Press. This book was released on 2015-05-07 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl
Book Synopsis Applied Biclustering Methods for Big and High-Dimensional Data Using R by : Adetayo Kasim
Download or read book Applied Biclustering Methods for Big and High-Dimensional Data Using R written by Adetayo Kasim and published by CRC Press. This book was released on 2016-10-03 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proven Methods for Big Data Analysis As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find local patterns in a big data matrix. The book presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to technical details of the methods are provided for readers who wish to investigate the full theoretical background. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website.
Book Synopsis Proceedings of the Second International Forum on Financial Mathematics and Financial Technology by : Zhiyong Zheng
Download or read book Proceedings of the Second International Forum on Financial Mathematics and Financial Technology written by Zhiyong Zheng and published by Springer Nature. This book was released on 2023-07-25 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is the documentary of the Second International Forum on Financial Mathematics and Financial Technology, with focus on selected aspects of the current and upcoming trends in FinTech. In detail, the included scientific papers cover financial mathematics and FinTech, presenting the innovative mathematical models and state-of-the-art technologies such as deep learning, with the aim to improve the financial analysis and decision-making and enhance the quality of financial services and risk control. The variety of the papers delivers added value for both scholars and practitioners where they will find perfect integration of elegant mathematical models and up-to-date data mining technologies in financial market analysis. Due to COVID-19, the conference was held virtually on August 13–15, 2021, jointly held by the School of Mathematics of Renmin University of China, the Engineering Research Center of Financial Computing and Digital Engineering of Ministry of Education, the Statistics and Big Data Research Institute of Renmin University of China, the Blockchain Research Institute of Renmin University of China, the Zhongguancun Internet Finance Research Institute, and the Renmin University Press.
Book Synopsis High-Dimensional Probability by : Roman Vershynin
Download or read book High-Dimensional Probability written by Roman Vershynin and published by Cambridge University Press. This book was released on 2018-09-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
Book Synopsis Big Data and Information Theory by : Jiuping Xu
Download or read book Big Data and Information Theory written by Jiuping Xu and published by Routledge. This book was released on 2022-06-02 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data and Information Theory are a binding force between various areas of knowledge that allow for societal advancement. Rapid development of data analytic and information theory allows companies to store vast amounts of information about production, inventory, service, and consumer activities. More powerful CPUs and cloud computing make it possible to do complex optimization instead of using heuristic algorithms, as well as instant rather than offline decision-making. The era of "big data" challenges includes analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations. Big data calls for better integration of optimization, statistics, and data mining. In response to these challenges this book brings together leading researchers and engineers to exchange and share their experiences and research results about big data and information theory applications in various areas. This book covers a broad range of topics including statistics, data mining, data warehouse implementation, engineering management in large-scale infrastructure systems, data-driven sustainable supply chain network, information technology service offshoring project issues, online rumors governance, preliminary cost estimation, and information system project selection. The chapters in this book were originally published in the journal, International Journal of Management Science and Engineering Management.
Book Synopsis Big and Complex Data Analysis by : S. Ejaz Ahmed
Download or read book Big and Complex Data Analysis written by S. Ejaz Ahmed and published by Springer. This book was released on 2017-03-21 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.
Book Synopsis Introduction to High-Dimensional Statistics by : Christophe Giraud
Download or read book Introduction to High-Dimensional Statistics written by Christophe Giraud and published by CRC Press. This book was released on 2021-08-25 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the first edition: "[This book] succeeds singularly at providing a structured introduction to this active field of research. ... it is arguably the most accessible overview yet published of the mathematical ideas and principles that one needs to master to enter the field of high-dimensional statistics. ... recommended to anyone interested in the main results of current research in high-dimensional statistics as well as anyone interested in acquiring the core mathematical skills to enter this area of research." —Journal of the American Statistical Association Introduction to High-Dimensional Statistics, Second Edition preserves the philosophy of the first edition: to be a concise guide for students and researchers discovering the area and interested in the mathematics involved. The main concepts and ideas are presented in simple settings, avoiding thereby unessential technicalities. High-dimensional statistics is a fast-evolving field, and much progress has been made on a large variety of topics, providing new insights and methods. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this new edition: Offers revised chapters from the previous edition, with the inclusion of many additional materials on some important topics, including compress sensing, estimation with convex constraints, the slope estimator, simultaneously low-rank and row-sparse linear regression, or aggregation of a continuous set of estimators. Introduces three new chapters on iterative algorithms, clustering, and minimax lower bounds. Provides enhanced appendices, minimax lower-bounds mainly with the addition of the Davis-Kahan perturbation bound and of two simple versions of the Hanson-Wright concentration inequality. Covers cutting-edge statistical methods including model selection, sparsity and the Lasso, iterative hard thresholding, aggregation, support vector machines, and learning theory. Provides detailed exercises at the end of every chapter with collaborative solutions on a wiki site. Illustrates concepts with simple but clear practical examples.
Book Synopsis Handbook of Bayesian Variable Selection by : Mahlet G. Tadesse
Download or read book Handbook of Bayesian Variable Selection written by Mahlet G. Tadesse and published by CRC Press. This book was released on 2021-12-24 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the mechanisms underlying an observed phenomenon. Variable selection is especially important when the number of potential predictors is substantially larger than the sample size and sparsity can reasonably be assumed. The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. The topics covered include spike-and-slab priors, continuous shrinkage priors, Bayes factors, Bayesian model averaging, partitioning methods, as well as variable selection in decision trees and edge selection in graphical models. The handbook targets graduate students and established researchers who seek to understand the latest developments in the field. It also provides a valuable reference for all interested in applying existing methods and/or pursuing methodological extensions. Features: Provides a comprehensive review of methods and applications of Bayesian variable selection. Divided into four parts: Spike-and-Slab Priors; Continuous Shrinkage Priors; Extensions to various Modeling; Other Approaches to Bayesian Variable Selection. Covers theoretical and methodological aspects, as well as worked out examples with R code provided in the online supplement. Includes contributions by experts in the field. Supported by a website with code, data, and other supplementary material
Book Synopsis Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data by : Syed Ejaz Ahmed
Download or read book Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data written by Syed Ejaz Ahmed and published by CRC Press. This book was released on 2023-05-25 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents some post-estimation and predictions strategies for the host of useful statistical models with applications in data science. It combines statistical learning and machine learning techniques in a unique and optimal way. It is well-known that machine learning methods are subject to many issues relating to bias, and consequently the mean squared error and prediction error may explode. For this reason, we suggest shrinkage strategies to control the bias by combining a submodel selected by a penalized method with a model with many features. Further, the suggested shrinkage methodology can be successfully implemented for high dimensional data analysis. Many researchers in statistics and medical sciences work with big data. They need to analyse this data through statistical modelling. Estimating the model parameters accurately is an important part of the data analysis. This book may be a repository for developing improve estimation strategies for statisticians. This book will help researchers and practitioners for their teaching and advanced research, and is an excellent textbook for advanced undergraduate and graduate courses involving shrinkage, statistical, and machine learning. The book succinctly reveals the bias inherited in machine learning method and successfully provides tools, tricks and tips to deal with the bias issue. Expertly sheds light on the fundamental reasoning for model selection and post estimation using shrinkage and related strategies. This presentation is fundamental, because shrinkage and other methods appropriate for model selection and estimation problems and there is a growing interest in this area to fill the gap between competitive strategies. Application of these strategies to real life data set from many walks of life. Analytical results are fully corroborated by numerical work and numerous worked examples are included in each chapter with numerous graphs for data visualization. The presentation and style of the book clearly makes it accessible to a broad audience. It offers rich, concise expositions of each strategy and clearly describes how to use each estimation strategy for the problem at hand. This book emphasizes that statistics/statisticians can play a dominant role in solving Big Data problems, and will put them on the precipice of scientific discovery. The book contributes novel methodologies for HDDA and will open a door for continued research in this hot area. The practical impact of the proposed work stems from wide applications. The developed computational packages will aid in analyzing a broad range of applications in many walks of life.
Book Synopsis High-Dimensional Covariance Estimation by : Mohsen Pourahmadi
Download or read book High-Dimensional Covariance Estimation written by Mohsen Pourahmadi and published by John Wiley & Sons. This book was released on 2013-05-28 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.
Book Synopsis Global Business Intelligence by : J Mark Munoz
Download or read book Global Business Intelligence written by J Mark Munoz and published by Routledge. This book was released on 2017-11-10 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Global Business Intelligence refers to an organization’s ability to gather, process and analyze pertinent international information in order to make optimal business decisions in a timely manner. With a challenging economic and geopolitical environment, companies and executives need to be adept at information gathering in order to manage emerging challenges and gain competitive advantages. This book Global Business Intelligence assembles a cast of international experts and thought leaders and explores the implications of business intelligence on contemporary management. Global Business Intelligence will be a key resource for researchers, academics, students and policy makers alike in the fields of International Business & Management, Business Strategy, and Geopolitics as well as related disciplines like Political Science, Economics, and Geography.
Book Synopsis Celebrating Statistics by : A. C. Davison
Download or read book Celebrating Statistics written by A. C. Davison and published by Oxford University Press, USA. This book was released on 2005-09-22 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sir David Cox is among the most important statisticians of the past half-century. He has made pioneering and highly influential contributions to a uniquely wide range of topics in statistics and applied probability. His teaching has inspired generations of students, and many well-known researchers have begun as his graduate students or have worked with him at early stages of their careers. Legions of others have been stimulated and enlightened by the clear, concise, and directexposition exemplified by his many books, papers, and lectures. This book presents a collection of chapters by major statistical researchers who attended a conference held at the University of Neuchatel in July 2004 to celebrate David Cox's 80th birthday. Each chapter is carefully crafted and collectivelypresent current developments across a wide range of research areas from epidemiology, environmental science, finance, computing and medicine.Edited by Anthony Davison, Ecole Polytechnique Federale de Lausanne, Switzerland; Yadolah Dodge, University of Neuchatel, Switzerland; and N. Wermuth, Goteborg University, Sweden, with chapters by Ole E. Barndorff-Nielsen, Sarah C. Darby, Christina Davies, Peter J. Diggle, David Firth, Peter Hall, Valerie S. Isham, Kung-Yee Liang, Peter McCullagh, Paul McGale, Amilcare Porporato, Nancy Reid, Brian D. Ripley, Ignacio Rodriguez-Iturbe, Andrea Rotnitzky, Neil Shephard, Scott L. Zeger, andincluding a brief biography of David Cox, this book is suitable for students of statistics, epidemiology, environmental science, finance, computing and medicine, and academic and practising statisticians.
Book Synopsis Statistical Foundations of Data Science by : Jianqing Fan
Download or read book Statistical Foundations of Data Science written by Jianqing Fan and published by CRC Press. This book was released on 2020-09-21 with total page 942 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Book Synopsis Innovations in Multivariate Statistical Modeling by : Andriëtte Bekker
Download or read book Innovations in Multivariate Statistical Modeling written by Andriëtte Bekker and published by Springer Nature. This book was released on 2022-12-15 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate statistical analysis has undergone a rich and varied evolution during the latter half of the 20th century. Academics and practitioners have produced much literature with diverse interests and with varying multidisciplinary knowledge on different topics within the multivariate domain. Due to multivariate algebra being of sustained interest and being a continuously developing field, its appeal breaches laterally across multiple disciplines to act as a catalyst for contemporary advances, with its core inferential genesis remaining in that of statistics. It is exactly this varied evolution caused by an influx in data production, diffusion, and understanding in scientific fields that has blurred many lines between disciplines. The cross-pollination between statistics and biology, engineering, medical science, computer science, and even art, has accelerated the vast amount of questions that statistical methodology has to answer and report on. These questions are often multivariate in nature, hoping to elucidate uncertainty on more than one aspect at the same time, and it is here where statistical thinking merges mathematical design with real life interpretation for understanding this uncertainty. Statistical advances benefit from these algebraic inventions and expansions in the multivariate paradigm. This contributed volume aims to usher novel research emanating from a multivariate statistical foundation into the spotlight, with particular significance in multidisciplinary settings. The overarching spirit of this volume is to highlight current trends, stimulate a focus on, and connect multidisciplinary dots from and within multivariate statistical analysis. Guided by these thoughts, a collection of research at the forefront of multivariate statistical thinking is presented here which has been authored by globally recognized subject matter experts.
Book Synopsis Issues in Statistics, Decision Making, and Stochastics: 2013 Edition by :
Download or read book Issues in Statistics, Decision Making, and Stochastics: 2013 Edition written by and published by ScholarlyEditions. This book was released on 2013-05-01 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues in Statistics, Decision Making, and Stochastics: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Regular and Chaotic Dynamics. The editors have built Issues in Statistics, Decision Making, and Stochastics: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Regular and Chaotic Dynamics in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Statistics, Decision Making, and Stochastics: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.
Book Synopsis Handbook of Research on Optimizing Healthcare Management Techniques by : Wickramasinghe, Nilmini
Download or read book Handbook of Research on Optimizing Healthcare Management Techniques written by Wickramasinghe, Nilmini and published by IGI Global. This book was released on 2019-12-27 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Healthcare is noted for using leading-edge technologies and embracing new scientific discoveries to enable better cures for diseases and better means to enable early detection of most life-threatening diseases. However, the healthcare industry globally, and in the US specifically, has been extremely slow to adopt technologies that focus on better practice management and administrative needs. Presently, healthcare is grappling with many challenges both nationally and globally, including escalating costs, a move to a preventative care environment, and a technologically savvy patient with high expectations. The Handbook of Research on Optimizing Healthcare Management Techniques is a pivotal reference source that provides an extensive and rich compilation of various ICT initiatives and examines the role that ICT plays and will play in the future of healthcare delivery. It represents ways in which healthcare delivery can be made superior and the healthcare industry can begin to address the major challenges it faces in the 21st century so that ultimately the most important person in the web of healthcare players, the patient, can be confident about receiving high-quality, cost-effective healthcare. While highlighting topics such as e-health, medical informatics, and patient value, this publication explores the role of supportive technologies as well as the methods of focused, patient-centric outcomes. This book is ideally designed for doctors, nurses, hospital administrators, medical staff, hospital directors, medical boards, IT consultants, health practitioners, academicians, researchers, and students.
