Closeness Of Factor Analysis And Principal Component Analysis In Semi High Dimensional Conditions
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Closeness Of Factor Analysis And Principal Component Analysis In Semi High Dimensional Conditions
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Author : Lu Liang
language : en
Publisher:
Release Date : 2016
Closeness Of Factor Analysis And Principal Component Analysis In Semi High Dimensional Conditions written by Lu Liang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Principal Component Analysis
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Author : I.T. Jolliffe
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Principal Component Analysis written by I.T. Jolliffe and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Mathematics categories.
Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri vations. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters.
Large Dimensional Factor Analysis
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Author : Jushan Bai
language : en
Publisher: Now Publishers Inc
Release Date : 2008
Large Dimensional Factor Analysis written by Jushan Bai and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Business & Economics categories.
Large Dimensional Factor Analysis provides a survey of the main theoretical results for large dimensional factor models, emphasizing results that have implications for empirical work. The authors focus on the development of the static factor models and on the use of estimated factors in subsequent estimation and inference. Large Dimensional Factor Analysis discusses how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy pf observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models.
Principal Components Analysis
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Author : George H. Dunteman
language : en
Publisher: SAGE
Release Date : 1989-05
Principal Components Analysis written by George H. Dunteman and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989-05 with Mathematics categories.
For anyone in need of a concise, introductory guide to principal components analysis, this book is a must. Through an effective use of simple mathematical-geometrical and multiple real-life examples (such as crime statistics, indicators of drug abuse, and educational expenditures) -- and by minimizing the use of matrix algebra -- the reader can quickly master and put this technique to immediate use.
Proceedings Of The 2012 International Conference On Cybernetics And Informatics
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Author : Shaobo Zhong
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-08-23
Proceedings Of The 2012 International Conference On Cybernetics And Informatics written by Shaobo Zhong and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-23 with Technology & Engineering categories.
Proceedings of the International Conference on Cybernetics and Informatics (ICCI 2012) covers the hybridization in control, computer, information, communications and applications. ICCI 2012 held on September 21-23, 2012, in Chongqing, China, is organized by Chongqing Normal University, Chongqing University, Nanyang Technological University, Shanghai Jiao Tong University, Hunan Institute of Engineering, Beijing University, and sponsored by National Natural Science Foundation of China (NSFC). This two volume publication includes selected papers from the ICCI 2012. Covering the latest research advances in the area of computer, informatics, cybernetics and applications, which mainly includes the computer, information, control, communications technologies and applications.
Post Shrinkage Strategies In Statistical And Machine Learning For High Dimensional Data
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Author : Syed Ejaz Ahmed
language : en
Publisher: CRC Press
Release Date : 2023-05-25
Post Shrinkage Strategies In Statistical And Machine Learning For High Dimensional Data written by Syed Ejaz Ahmed and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-25 with Business & Economics categories.
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.
Three Mode Principal Component Analysis
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Author : Pieter M. Kroonenberg
language : en
Publisher:
Release Date : 1983
Three Mode Principal Component Analysis written by Pieter M. Kroonenberg and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Multivariate analysis categories.
Advances In Principal Component Analysis
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Author : Ganesh R. Naik
language : en
Publisher: Springer
Release Date : 2017-12-11
Advances In Principal Component Analysis written by Ganesh R. Naik and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-11 with Technology & Engineering categories.
This book reports on the latest advances in concepts and further developments of principal component analysis (PCA), addressing a number of open problems related to dimensional reduction techniques and their extensions in detail. Bringing together research results previously scattered throughout many scientific journals papers worldwide, the book presents them in a methodologically unified form. Offering vital insights into the subject matter in self-contained chapters that balance the theory and concrete applications, and especially focusing on open problems, it is essential reading for all researchers and practitioners with an interest in PCA.
Ibm Spss For Introductory Statistics
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Author : George A. Morgan
language : en
Publisher: Routledge
Release Date : 2012-09-10
Ibm Spss For Introductory Statistics written by George A. Morgan and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-10 with Psychology categories.
Designed to help students analyze and interpret research data using IBM SPSS, this user-friendly book, written in easy-to-understand language, shows readers how to choose the appropriate statistic based on the design, and to interpret outputs appropriately. The authors prepare readers for all of the steps in the research process: design, entering and checking data, testing assumptions, assessing reliability and validity, computing descriptive and inferential parametric and nonparametric statistics, and writing about outputs. Dialog windows and SPSS syntax, along with the output, are provided. Three realistic data sets, available on the Internet, are used to solve the chapter problems. The new edition features: Updated to IBM SPSS version 20 but the book can also be used with older and newer versions of SPSS. A new chapter (7) including an introduction to Cronbach’s alpha and factor analysis. Updated Web Resources with PowerPoint slides, additional activities/suggestions, and the answers to even-numbered interpretation questions for the instructors, and chapter study guides and outlines and extra SPSS problems for the students. The web resource is located www.routledge.com/9781848729827 . Students, instructors, and individual purchasers can access the data files to accompany the book at www.routledge.com/9781848729827 . IBM SPSS for Introductory Statistics, Fifth Edition provides helpful teaching tools: All of the key IBM SPSS windows needed to perform the analyses. Complete outputs with call-out boxes to highlight key points. Flowcharts and tables to help select appropriate statistics and interpret effect sizes. Interpretation sections and questions help students better understand and interpret the output. Assignments organized the way students proceed when they conduct a research project. Examples of how to write about outputs and make tables in APA format. Helpful appendices on how to get started with SPSS and write research questions. An ideal supplement for courses in either statistics, research methods, or any course in which SPSS is used, such as in departments of psychology, education, and other social and health sciences. This book is also appreciated by researchers interested in using SPSS for their data analysis.
Generalized Principal Component Analysis
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Author : René Vidal
language : en
Publisher: Springer
Release Date : 2016-04-11
Generalized Principal Component Analysis written by René Vidal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-11 with Science categories.
This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc. This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book. René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.