Download or read book Multivariate Analysis of Variance and Robust Estimation of Covariance Structures when the Data are Curves written by Minggao Shi and published by . This book was released on 1993 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Exploring Multivariate Data with the Forward Search written by Anthony C. Atkinson and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 642 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is concerned with data in which the observations are independent and in which the response is multivariate. Companion book to Robust Diagnostic Regression Analysis (ISBN 0-387-95017) published by Springer in 2000.
Download or read book Applied Multivariate Analysis written by Neil H. Timm and published by Springer Science & Business Media. This book was released on 2007-06-21 with total page 709 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates both theory and practice including both the analysis of formal linear multivariate models and exploratory data analysis techniques. Each chapter contains the development of basic theoretical results with numerous applications illustrated using examples from the social and behavioral sciences, and other disciplines. All examples are analyzed using SAS for Windows Version 8.0.
Download or read book Analysis of Variance written by P. R. Krishnaiah and published by . This book was released on 1980 with total page 1002 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimation of variance components; Multivariate analysis of variance of repeated measurements; Growth curve analysis; Bayesian inference in MANOVA; Graphical methods for internal comparisons in ANOVA and MANOVA; Monotonicity and unbiasedness properties of ANOVA and MANOVA; Robustness of ANOVA and MANOVA test procedures; Analysis of variance and problems under time series models; Tests of unvariate and multivariate normality; Transformations to normality; ANOVA and MANOVA: models for categorical data; Inference and the structural model for ANOVA and MANOVA; Inference based on conditionally specified ANOVA models incorporating preliminary testing; Quadratic forms in normal variables; Generalized inverse of matrices and applications to linear models; Likelihood ratio tests for mean vectors and covariance matrices; Assessing dimensionality in multivariate regression; Parameter estimation in nonlinear regression models; Early history of multiple comparison tests; Representation of simultaneous pairwise comparisons; Simultaneous test procedures for mean vectors and covariance matrices; Nonparametric simultaneous inference for some MANOVA models ...
Download or read book Directions in Robust Statistics and Diagnostics written by Werner Stahel and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IMA Volume in Mathematics and its Applications DIRECTIONS IN ROBUST STATISTICS AND DIAGNOSTICS is based on the proceedings of the first four weeks of the six week IMA 1989 summer program "Robustness, Diagnostics, Computing and Graphics in Statistics". An important objective of the organizers was to draw a broad set of statisticians working in robustness or diagnostics into collaboration on the challenging problems in these areas, particularly on the interface between them. We thank the organizers of the robustness and diagnostics program Noel Cressie, Thomas P. Hettmansperger, Peter J. Huber, R. Douglas Martin, and especially Werner Stahel and Sanford Weisberg who edited the proceedings. A vner Friedman Willard Miller, Jr. PREFACE Central themes of all statistics are estimation, prediction, and making decisions under uncertainty. A standard approach to these goals is through parametric mod elling. Parametric models can give a problem sufficient structure to allow standard, well understood paradigms to be applied to make the required inferences. If, how ever, the parametric model is not completely correct, then the standard inferential methods may not give reasonable answers. In the last quarter century, particularly with the advent of readily available computing, more attention has been paid to the problem of inference when the parametric model used is not correctly specified.
Download or read book Multivariate Analysis Future Directions 2 written by C.M. Cuadras and published by Elsevier. This book was released on 2014-05-21 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributions in this volume, made by distinguished statisticians in several frontier areas of research in multivariate analysis, cover a broad field and indicate future directions of research. The topics covered include discriminant analysis, multidimensional scaling, categorical data analysis, correspondence analysis and biplots, association analysis, latent variable models, bootstrap distributions, differential geometry applications and others. Most of the papers propose generalizations or new applications of multivariate analysis. This volume will be of great interest to statisticians, probabilists, data analysts and scientists working in the disciplines such as biology, biometry, ecology, medicine, econometry, psychometry and marketing. It will be a valuable guide to professors, researchers and graduate students seeking new and promising lines of statistical research.
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.
Download or read book Introduction to Multivariate Analysis written by Chris Chatfield and published by Routledge. This book was released on 2018-02-19 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the analysis of multivariate data.It describes multivariate probability distributions, the preliminary analysisof a large -scale set of data, princ iple component and factor analysis,traditional normal theory material, as well as multidimensional scaling andcluster analysis.Introduction to Multivariate Analysis provides a reasonable blend oftheory and practice. Enough theory is given to introduce the concepts andto make the topics mathematically interesting. In addition the authors discussthe use (and misuse) of the techniques in pra ctice and present appropriatereal-life examples from a variety of areas includ ing agricultural research,soc iology and crim inology. The book should be suitable both for researchworkers and as a text for students taking a course on multivariate analysis.
Download or read book Multivariate Density Estimation written by David W. Scott and published by John Wiley & Sons. This book was released on 2015-03-12 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis. The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition also features: Over 150 updated figures to clarify theoretical results and to show analyses of real data sets An updated presentation of graphic visualization using computer software such as R A clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering More than 130 problems to help readers reinforce the main concepts and ideas presented Boxed theorems and results allowing easy identification of crucial ideas Figures in color in the digital versions of the book A website with related data sets Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The Second Edition is also useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.
Download or read book Univariate and Multivariate Methods for the Analysis of Repeated Measures Data written by Tony Wragg and published by GRIN Verlag. This book was released on 2007-03-09 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thesis (M.A.) from the year 1999 in the subject Mathematics - Statistics, grade: Passed, RMIT, course: MAppSc, language: English, abstract: This thesis considers both univariate and multivariate approaches to the analysis of a set of repeated-measures data. Since repeated measures on the same subject are correlated over time, the usual analysis of variance assumption of independence is often violated. The models in this thesis demonstrate different approaches to the analysis of repeated-measures data, and highlight their advantages and disadvantages. Milk from two groups of lactating cows, one group vaccinated, the other not, was analysed every month after calving for eight months in order to measure the amount of bacteria in the milk. The primary goal of the experiment was to determine if a vaccine developed by the Royal Melbourne Institute of Technology’s Biology Department led to a significant decrease in mean bacteria production per litre of milk produced compared to the control group. A univariate model suitable for repeated measures data was initially tried, with mean bacteria production in the treatment group not significantly different from the control group (p
Download or read book An Introduction to Applied Multivariate Analysis with R written by Brian Everitt and published by Springer Science & Business Media. This book was released on 2011-04-23 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
Download or read book Multivariate Analysis of Variance and Repeated Measures written by David J. Hand and published by CRC Press. This book was released on 1987-05-01 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes a practical aproach to univariate and multivariate analysis of variance. It starts with a general non-mathematical account of the fundamental theories and this is followed by a discussion of a series of examples using real data sets from the authors' own work in clinical trials, psychology and industry. Included are discussions of factorial and nested designs, structures on the multiple dependent variables measured on each subject, repeated measures analyses, covariates, choice of text statistic and simultaneous test procedures.
Download or read book Multivariate Analysis III written by Paruchuri R. Krishnaiah and published by Academic Press. This book was released on 2014-05-12 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate Analysis — III contains the proceedings of the Third International Symposium on Multivariate Analysis held at Wright State University in Dayton, Ohio, on June 19-24, 1972. The papers explore the theory and applications of multivariate analysis and cover areas such as time series and stochastic processes; distribution theory and inference; characteristic functions and characterizations; and design and analysis of experiments. Classification, modeling, and reliability are also discussed. Comprised of 27 chapters, this volume begins with an introduction to two-dimensional random fields, giving results for a class of Gaussian processes with a multidimensional time parameter. The next chapter deals with concepts of consistency in spectral estimation for multivariate time series and considers the alternative of estimating the spectral distribution function or the spectral density function. Abstract martingales and ergodic theory are also examined, along with methods for assessing multivariate normality; inference and redundant parameters; characterization of the multivariate geometric distribution; and max-min designs in the analysis of variance. This monograph will be useful to statisticians and probabilists, as well as to scientists in other disciplines who are broadly interested in multivariate analysis.
Download or read book Applied Multivariate Statistical Analysis written by Wolfgang Karl Härdle and published by Springer Nature. This book was released on with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multivariate Analysis of Variance written by James H. Bray and published by SAGE. This book was released on 1985 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bray's monograph considers the multivariate form of analysis of variance (MANOVA). It is a technique which can be used in such different academic disciplines as psychology, sociology, biology, and education.
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2006 with total page 848 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Analyzing Multivariate Data written by Norman Cliff and published by . This book was released on 1987 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt:
