Discriminant Analysis with Discrete and Continuous Data
| Author | : Donald Michael Stablein |
| Publisher | : |
| Total Pages | : 88 |
| Release | : 1977 |
| Genre | : |
| ISBN | : |
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Discriminant Analysis With Discrete And Continuous Data PDF Download
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A Hypothesis-Testing Approach to Discriminant Analysis with Mixed Categorical and Continuous Variables When Data are Missing
| Author | : |
| Publisher | : |
| Total Pages | : 41 |
| Release | : 1994 |
| Genre | : |
| ISBN | : |
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In this report we consider the problem of discriminant analysis with discrete (categorical) and continuous variables with data missing at random. We use a hypothesis-testing approach based on the generalized likelihood ratio as proposed by Baek, et al. We use bootstrapping to determine critical values in order to control the Type 1 error rate. We present three algorithms for dealing with this case, each assuming a different model for the data: the INDICATOR algorithm replaces categorical variables with indicator variables, and treats these as if they were continuous, the FULL algorithm assumes a multinomial distribution for the discrete part, and a multivariate normal distribution (with mean and covariances depending on the discrete part) as the conditional distribution of the continuous part given the discrete part, and the COMMON algorithm assumes a multinomial distribution for the discrete part, and a multivariate normal distribution (with only the means depending on the discrete part) as the conditional distribution of the continuous part given the discrete part. (That is, a common covariance matrix is assumed across all multinomial cells.) The performance of these algorithms is compared through a simulation study. While the INDICATOR algorithm seems to have highest power, it also tends to display a higher Type 1 error rate than desired. The FULL and the COMMON algorithms have very similar power, but the COMMON algorithm appears to control the Type 1 error rate most effectively, and is least susceptible to problems occurring when some multinomial cells are sparsely represented. (AN).
Discriminant Analysis and Statistical Pattern Recognition
| Author | : Geoffrey J. McLachlan |
| Publisher | : John Wiley & Sons |
| Total Pages | : 552 |
| Release | : 2005-02-25 |
| Genre | : Mathematics |
| ISBN | : 0471725285 |
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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.
Statistical Methods of Discrimination and Classification
| Author | : Sung C. Choi |
| Publisher | : Elsevier |
| Total Pages | : 145 |
| Release | : 2014-05-17 |
| Genre | : Mathematics |
| ISBN | : 1483190986 |
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Statistical Methods of Discrimination and Classification: Advances in Theory and Applications is a collection of papers that tackles the multivariate problems of discriminating and classifying subjects into exclusive population. The book presents 13 papers that cover that advancement in the statistical procedure of discriminating and classifying. The studies in the text primarily focus on various methods of discriminating and classifying variables, such as multiple discriminant analysis in the presence of mixed continuous and categorical data; choice of the smoothing parameter and efficiency of k-nearest neighbor classification; and assessing the performance of an allocation rule. The book will be of great use to researchers and practitioners of wide array of scientific disciplines, including engineering, psychology, biology, and physics.
Discriminant Analysis and Statistical Pattern Recognition
| Author | : Geoffrey McLachlan |
| Publisher | : John Wiley & Sons |
| Total Pages | : 526 |
| Release | : 2005-02-25 |
| Genre | : Mathematics |
| ISBN | : 0471725285 |
Download Discriminant Analysis and Statistical Pattern Recognition Book in PDF, ePub and Kindle
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.
Discriminant Analysis and Applications
| Author | : T. Cacoullos |
| Publisher | : Academic Press |
| Total Pages | : 455 |
| Release | : 2014-05-10 |
| Genre | : Mathematics |
| ISBN | : 1483268713 |
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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.
Statistical Pattern Recognition
| Author | : Andrew R. Webb |
| Publisher | : Newnes |
| Total Pages | : 476 |
| Release | : 1999 |
| Genre | : Computers |
| ISBN | : 9780340741641 |
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"This book provides an introduction to statistical pattern recognition theory and techniques. Most of the material presented in this book is concerned with discrimination and classification and has been drawn from a wide range of literature including that of engineering, statistics, computer science and the social sciences. This book is an attempt to provide a concise volume containing descriptions of many of the most useful of today's pattern processing techniques including many of the recent advances in nonparametric approaches to discrimination developed in the statistics literature and elsewhere. The techniques are illustrated with examples of real-world applications studies. Pointers are also provided to the diverse literature base where further details on applications, comparative studies and theoretical developments may be obtained"--Page [xv].
An Introduction to Categorical Data Analysis
| Author | : Alan Agresti |
| Publisher | : John Wiley & Sons |
| Total Pages | : 423 |
| Release | : 2018-10-11 |
| Genre | : Mathematics |
| ISBN | : 1119405289 |
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A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
Principles of Data Mining
| Author | : David J. Hand |
| Publisher | : MIT Press |
| Total Pages | : 594 |
| Release | : 2001-08-17 |
| Genre | : Computers |
| ISBN | : 9780262082907 |
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The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
Best Practices in Quantitative Methods
| Author | : Jason W. Osborne |
| Publisher | : SAGE |
| Total Pages | : 609 |
| Release | : 2008 |
| Genre | : Social Science |
| ISBN | : 1412940656 |
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The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically. Editor Jason W. Osborne designed this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences. The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. Each chapter contains a current and expansive review of the literature, a case for best practices in terms of method, outcomes, inferences, etc., and broad-ranging examples along with any empirical evidence to show why certain techniques are better. Key Features: Describes important implicit knowledge to readers: The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details. Compares and contrasts analytic techniques: The book examines instances where there are multiple options for doing things, and make recommendations as to what is the "best" choice—or choices, as what is best often depends on the circumstances. Offers new procedures to update and explicate traditional techniques: The featured scholars present and explain new options for data analysis, discussing the advantages and disadvantages of the new procedures in depth, describing how to perform them, and demonstrating their use. Intended Audience: Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods.
