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Discriminant Analysis
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Book Synopsis Modern Multivariate Statistical Techniques by : Alan J. Izenman
Download or read book Modern Multivariate Statistical Techniques written by Alan J. Izenman and published by Springer Science & Business Media. This book was released on 2009-03-02 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.
Book Synopsis Discriminant Analysis and Applications by : T. Cacoullos
Download or read book Discriminant Analysis and Applications written by T. Cacoullos and published by Academic Press. This book was released on 2014-05-10 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discriminant Analysis and Applications comprises the proceedings of the NATO Advanced Study Institute on Discriminant Analysis and Applications held in Kifissia, Athens, Greece in June 1972. The book presents the theory and applications of Discriminant analysis, one of the most important areas of multivariate statistical analysis. This volume contains chapters that cover the historical development of discriminant analysis methods; logistic and quasi-linear discrimination; and distance functions. Medical and biological applications, and computer graphical analysis and graphical techniques for multidimensional data are likewise discussed. Statisticians, mathematicians, and biomathematicians will find the book very interesting.
Book Synopsis Discriminant Analysis and Statistical Pattern Recognition by : Geoffrey J. McLachlan
Download or read book Discriminant Analysis and Statistical Pattern Recognition written by Geoffrey J. McLachlan and published by John Wiley & Sons. This book was released on 2005-02-25 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "For both applied and theoretical statisticians as well as investigators working in the many areas in which relevant use can be made of discriminant techniques, this monograph provides a modern, comprehensive, and systematic account of discriminant analysis, with the focus on the more recent advances in the field." –SciTech Book News ". . . a very useful source of information for any researcher working in discriminant analysis and pattern recognition." –Computational Statistics Discriminant Analysis and Statistical Pattern Recognition provides a systematic account of the subject. While the focus is on practical considerations, both theoretical and practical issues are explored. Among the advances covered are regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule, and extensions of discriminant analysis motivated by problems in statistical image analysis. The accompanying bibliography contains over 1,200 references.
Book Synopsis Discriminant Analysis by : William R. Klecka
Download or read book Discriminant Analysis written by William R. Klecka and published by SAGE. This book was released on 1980-08 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: Background. Deriving the canonical discriminant functions. Interpreting the canonical discriminant functions. Classification procedures. Stepwise inclusion of variables. Concluding remarks.
Book Synopsis Applied MANOVA and Discriminant Analysis by : Carl J. Huberty
Download or read book Applied MANOVA and Discriminant Analysis written by Carl J. Huberty and published by John Wiley & Sons. This book was released on 2006-05-12 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete introduction to discriminant analysis--extensively revised, expanded, and updated This Second Edition of the classic book, Applied Discriminant Analysis, reflects and references current usage with its new title, Applied MANOVA and Discriminant Analysis. Thoroughly updated and revised, this book continues to be essential for any researcher or student needing to learn to speak, read, and write about discriminant analysis as well as develop a philosophy of empirical research and data analysis. Its thorough introduction to the application of discriminant analysis is unparalleled. Offering the most up-to-date computer applications, references, terms, and real-life research examples, the Second Edition also includes new discussions of MANOVA, descriptive discriminant analysis, and predictive discriminant analysis. Newer SAS macros are included, and graphical software with data sets and programs are provided on the book's related Web site. The book features: Detailed discussions of multivariate analysis of variance and covariance An increased number of chapter exercises along with selected answers Analyses of data obtained via a repeated measures design A new chapter on analyses related to predictive discriminant analysis Basic SPSS(r) and SAS(r) computer syntax and output integrated throughout the book Applied MANOVA and Discriminant Analysis enables the reader to become aware of various types of research questions using MANOVA and discriminant analysis; to learn the meaning of this field's concepts and terms; and to be able to design a study that uses discriminant analysis through topics such as one-factor MANOVA/DDA, assessing and describing MANOVA effects, and deleting and ordering variables.
Book Synopsis New Theory of Discriminant Analysis After R. Fisher by : Shuichi Shinmura
Download or read book New Theory of Discriminant Analysis After R. Fisher written by Shuichi Shinmura and published by Springer. This book was released on 2016-12-27 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book to compare eight LDFs by different types of datasets, such as Fisher’s iris data, medical data with collinearities, Swiss banknote data that is a linearly separable data (LSD), student pass/fail determination using student attributes, 18 pass/fail determinations using exam scores, Japanese automobile data, and six microarray datasets (the datasets) that are LSD. We developed the 100-fold cross-validation for the small sample method (Method 1) instead of the LOO method. We proposed a simple model selection procedure to choose the best model having minimum M2 and Revised IP-OLDF based on MNM criterion was found to be better than other M2s in the above datasets. We compared two statistical LDFs and six MP-based LDFs. Those were Fisher’s LDF, logistic regression, three SVMs, Revised IP-OLDF, and another two OLDFs. Only a hard-margin SVM (H-SVM) and Revised IP-OLDF could discriminate LSD theoretically (Problem 2). We solved the defect of the generalized inverse matrices (Problem 3). For more than 10 years, many researchers have struggled to analyze the microarray dataset that is LSD (Problem 5). If we call the linearly separable model "Matroska," the dataset consists of numerous smaller Matroskas in it. We develop the Matroska feature selection method (Method 2). It finds the surprising structure of the dataset that is the disjoint union of several small Matroskas. Our theory and methods reveal new facts of gene analysis.
Book Synopsis Discrete Discriminant Analysis by : Matthew Goldstein
Download or read book Discrete Discriminant Analysis written by Matthew Goldstein and published by John Wiley & Sons. This book was released on 1978 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: The linear discriminant function; Discrete classification models; Error rates and the problem of bias; The variable-selection problem; Special topics; Computer programs.
Book Synopsis Discriminant Analysis and Statistical Pattern Recognition by : Geoffrey J. McLachlan
Download or read book Discriminant Analysis and Statistical Pattern Recognition written by Geoffrey J. McLachlan and published by John Wiley & Sons. This book was released on 2005-02-25 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "For both applied and theoretical statisticians as well as investigators working in the many areas in which relevant use can be made of discriminant techniques, this monograph provides a modern, comprehensive, and systematic account of discriminant analysis, with the focus on the more recent advances in the field." –SciTech Book News ". . . a very useful source of information for any researcher working in discriminant analysis and pattern recognition." –Computational Statistics Discriminant Analysis and Statistical Pattern Recognition provides a systematic account of the subject. While the focus is on practical considerations, both theoretical and practical issues are explored. Among the advances covered are regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule, and extensions of discriminant analysis motivated by problems in statistical image analysis. The accompanying bibliography contains over 1,200 references.
Book Synopsis Machine Learning Techniques for Improved Business Analytics by : G., Dileep Kumar
Download or read book Machine Learning Techniques for Improved Business Analytics written by G., Dileep Kumar and published by IGI Global. This book was released on 2018-07-06 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analytical tools and algorithms are essential in business data and information systems. Efficient economic and financial forecasting in machine learning techniques increases gains while reducing risks. Providing research on predictive models with high accuracy, stability, and ease of interpretation is important in improving data preparation, analysis, and implementation processes in business organizations. Machine Learning Techniques for Improved Business Analytics is a collection of innovative research on the methods and applications of artificial intelligence in strategic business decisions and management. Featuring coverage on a broad range of topics such as data mining, portfolio optimization, and social network analysis, this book is ideally designed for business managers and practitioners, upper-level business students, and researchers seeking current research on large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques.
Book Synopsis Discriminant Analysis and Clustering by : Ram Gnanadesikan
Download or read book Discriminant Analysis and Clustering written by Ram Gnanadesikan and published by National Academies Press. This book was released on 1988-01-01 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applied Discriminant Analysis by : Carl J. Huberty
Download or read book Applied Discriminant Analysis written by Carl J. Huberty and published by Wiley-Interscience. This book was released on 1994-08-11 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most books on discriminant analysis focus on statistical theory. But properly applied, discriminant analysis methods can be enormously useful in the interpretation of data. This book is the first ever to offer a complete introduction to discriminant analysis that focuses on applications. It provides numerous examples, explained in great detail, using current statistical discriminant analysis algorithms. It also develops several themes that will be useful to researchers and students regardless of the analytical methods they employ. They are the careful examination of data prior to final analysis; the application of critical judgment and common sense to all analyses and interpretations; and conducting multiple analyses as a matter of routine. To encourage and enable readers to conduct multiple analyses of their data, the accompanying diskette contains the four complete data sets and five special computer programs that are referred to repeatedly in the text and are the subjects of numerous exercise problems. This enables the reader to carry out package analyses on the data sets using a variety of procedural options both within and across computer packages. The term "discriminant analysis" means different things to different people. For statisticians and researchers in the physical sciences, it usually denotes the process through which group membership is predicted on the basis of multiple predictor variables. Behavioral scientists, on the other hand, often use discriminant analysis to describe group differences across multiple response variables. Though closely related, predictive discriminant analysis (PDA) and descriptive discriminant analysis (DDA) are used for different purposes and should be approached in different ways. To accentuate these differences and distinguish clearly between the two, Applied Discriminant Analysis presents these topics separately. For graduate students, this book will expand your background in multivariate data analysis methods and facilitate both the reading and the conducting of applied empirical research. It will also be of great use to experienced researchers who wish to enhance or update their quantitative background, and to methodologists who want to learn more about the details of applied discriminant data analysis, and some still unresolved problems, as well.
Book Synopsis Statistical Pattern Recognition by : Andrew R. Webb
Download or read book Statistical Pattern Recognition written by Andrew R. Webb and published by John Wiley & Sons. This book was released on 2003-07-25 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition is a very active area of study andresearch, which has seen many advances in recent years. New andemerging applications - such as data mining, web searching,multimedia data retrieval, face recognition, and cursivehandwriting recognition - require robust and efficient patternrecognition techniques. Statistical decision making and estimationare regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fullyupdated with new methods, applications and references. It providesa comprehensive introduction to this vibrant area - with materialdrawn from engineering, statistics, computer science and the socialsciences - and covers many application areas, such as databasedesign, artificial neural networks, and decision supportsystems. * Provides a self-contained introduction to statistical patternrecognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vectormachines, and unsupervised classification. * Each section concludes with a description of the applicationsthat have been addressed and with further developments of thetheory. * Includes background material on dissimilarity, parameterestimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions tomore lengthy projects. The book is aimed primarily at senior undergraduate and graduatestudents studying statistical pattern recognition, patternprocessing, neural networks, and data mining, in both statisticsand engineering departments. It is also an excellent source ofreference for technical professionals working in advancedinformation development environments. For further information on the techniques and applicationsdiscussed in this book please visit ahref="http://www.statistical-pattern-recognition.net/"www.statistical-pattern-recognition.net/a
Book Synopsis R for Statistics by : Pierre-Andre Cornillon
Download or read book R for Statistics written by Pierre-Andre Cornillon and published by CRC Press. This book was released on 2012-03-21 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples. Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R. Focusing on the R software, the first section covers: Basic elements of the R software and data processing Clear, concise visualization of results, using simple and complex graphs Programming basics: pre-defined and user-created functions The second section of the book presents R methods for a wide range of traditional statistical data processing techniques, including: Regression methods Analyses of variance and covariance Classification methods Exploratory multivariate analysis Clustering methods Hypothesis tests After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist. Datasets and all the results described in this book are available on the book’s webpage at http://www.agrocampus-ouest.fr/math/RforStat
Book Synopsis Easy Statistics for Food Science with R by : Abbas F.M. Alkarkhi
Download or read book Easy Statistics for Food Science with R written by Abbas F.M. Alkarkhi and published by Academic Press. This book was released on 2018-09-18 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: Easy Statistics for Food Science with R presents the application of statistical techniques to assist students and researchers who work in food science and food engineering in choosing the appropriate statistical technique. The book focuses on the use of univariate and multivariate statistical methods in the field of food science. The techniques are presented in a simplified form without relying on complex mathematical proofs. This book was written to help researchers from different fields to analyze their data and make valid decisions. The development of modern statistical packages makes the analysis of data easier than before. The book focuses on the application of statistics and correct methods for the analysis and interpretation of data. R statistical software is used throughout the book to analyze the data. - Contains numerous step-by-step tutorials help the reader to learn quickly - Covers the theory and application of the statistical techniques - Shows how to analyze data using R software - Provides R scripts for all examples and figures
Book Synopsis Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes) by : Cheng Few Lee
Download or read book Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes) written by Cheng Few Lee and published by World Scientific. This book was released on 2020-07-30 with total page 5053 pages. Available in PDF, EPUB and Kindle. Book excerpt: This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.
Book Synopsis Mastering Machine Learning for Penetration Testing by : Chiheb Chebbi
Download or read book Mastering Machine Learning for Penetration Testing written by Chiheb Chebbi and published by Packt Publishing Ltd. This book was released on 2018-06-27 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.
Book Synopsis Handbook of Applied Multivariate Statistics and Mathematical Modeling by : Howard E.A. Tinsley
Download or read book Handbook of Applied Multivariate Statistics and Mathematical Modeling written by Howard E.A. Tinsley and published by Academic Press. This book was released on 2000-05-22 with total page 751 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate statistics and mathematical models provide flexible and powerful tools essential in most disciplines. Nevertheless, many practicing researchers lack an adequate knowledge of these techniques, or did once know the techniques, but have not been able to keep abreast of new developments. The Handbook of Applied Multivariate Statistics and Mathematical Modeling explains the appropriate uses of multivariate procedures and mathematical modeling techniques, and prescribe practices that enable applied researchers to use these procedures effectively without needing to concern themselves with the mathematical basis. The Handbook emphasizes using models and statistics as tools. The objective of the book is to inform readers about which tool to use to accomplish which task. Each chapter begins with a discussion of what kinds of questions a particular technique can and cannot answer. As multivariate statistics and modeling techniques are useful across disciplines, these examples include issues of concern in biological and social sciences as well as the humanities.
