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Multivariate Models And Multivariate Dependence Concepts
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Book Synopsis Multivariate Models and Multivariate Dependence Concepts by : Harry Joe
Download or read book Multivariate Models and Multivariate Dependence Concepts written by Harry Joe and published by CRC Press. This book was released on 1997-05-01 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book on multivariate models, statistical inference, and data analysis contains deep coverage of multivariate non-normal distributions for modeling of binary, count, ordinal, and extreme value response data. It is virtually self-contained, and includes many exercises and unsolved problems.
Book Synopsis Multivariate Models and Multivariate Dependence Concepts by : Harry Joe
Download or read book Multivariate Models and Multivariate Dependence Concepts written by Harry Joe and published by . This book was released on 1997 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book on multivariate models, statistical inference, and data analysis contains deep coverage of multivariate non-normal distributions for modeling of binary, count, ordinal, and extreme value response data. It is virtually self-contained, and includes many exercises and unsolved problems.
Book Synopsis Introduction to Probability by : Narayanaswamy Balakrishnan
Download or read book Introduction to Probability written by Narayanaswamy Balakrishnan and published by John Wiley & Sons. This book was released on 2021-11-24 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: INTRODUCTION TO PROBABILITY Discover practical models and real-world applications of multivariate models useful in engineering, business, and related disciplines In Introduction to Probability: Multivariate Models and Applications, a team of distinguished researchers delivers a comprehensive exploration of the concepts, methods, and results in multivariate distributions and models. Intended for use in a second course in probability, the material is largely self-contained, with some knowledge of basic probability theory and univariate distributions as the only prerequisite. This textbook is intended as the sequel to Introduction to Probability: Models and Applications. Each chapter begins with a brief historical account of some of the pioneers in probability who made significant contributions to the field. It goes on to describe and explain a critical concept or method in multivariate models and closes with two collections of exercises designed to test basic and advanced understanding of the theory. A wide range of topics are covered, including joint distributions for two or more random variables, independence of two or more variables, transformations of variables, covariance and correlation, a presentation of the most important multivariate distributions, generating functions and limit theorems. This important text: Includes classroom-tested problems and solutions to probability exercises Highlights real-world exercises designed to make clear the concepts presented Uses Mathematica software to illustrate the text’s computer exercises Features applications representing worldwide situations and processes Offers two types of self-assessment exercises at the end of each chapter, so that students may review the material in that chapter and monitor their progress Perfect for students majoring in statistics, engineering, business, psychology, operations research and mathematics taking a second course in probability, Introduction to Probability: Multivariate Models and Applications is also an indispensable resource for anyone who is required to use multivariate distributions to model the uncertainty associated with random phenomena.
Book Synopsis Number-Theoretic Methods in Statistics by : Kai-Tai Fang
Download or read book Number-Theoretic Methods in Statistics written by Kai-Tai Fang and published by CRC Press. This book was released on 1993-12-01 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a survey of recent work on the application of number theory in statistics. The essence of number-theoretic methods is to find a set of points that are universally scattered over an s-dimensional unit cube. In certain circumstances this set can be used instead of random numbers in the Monte Carlo method. The idea can also be applied to other problems such as in experimental design. This book will illustrate the idea of number-theoretic methods and their application in statistics. The emphasis is on applying the methods to practical problems so only part-proofs of theorems are given.
Book Synopsis Some Concepts of Multivariate Dependence by : Henry W. Block
Download or read book Some Concepts of Multivariate Dependence written by Henry W. Block and published by . This book was released on 1980 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: Several types of positive dependence are shown to be equivalent to the concept of a distribution with a density which is TP2 in pairs. Among these is the concept of m*-positive dependence of Alam and Wallenius Using this result, all relationships among many of the most important concepts of positive dependence are determined. Furthermore, an application of the equivalences of these types of positive dependence yields a result of Ahmed, Langberg, Leon and Proschan. (Author).
Book Synopsis Multivariate, Multilinear and Mixed Linear Models by : Katarzyna Filipiak
Download or read book Multivariate, Multilinear and Mixed Linear Models written by Katarzyna Filipiak and published by Springer. This book was released on 2022-10-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest findings on statistical inference in multivariate, multilinear and mixed linear models, providing a holistic presentation of the subject. It contains pioneering and carefully selected review contributions by experts in the field and guides the reader through topics related to estimation and testing of multivariate and mixed linear model parameters. Starting with the theory of multivariate distributions, covering identification and testing of covariance structures and means under various multivariate models, it goes on to discuss estimation in mixed linear models and their transformations. The results presented originate from the work of the research group Multivariate and Mixed Linear Models and their meetings held at the Mathematical Research and Conference Center in Będlewo, Poland, over the last 10 years. Featuring an extensive bibliography of related publications, the book is intended for PhD students and researchers in modern statistical science who are interested in multivariate and mixed linear models.
Book Synopsis L'-superadditive Functions and Concepts of Multivariate Dependence by : Raymond Walter Falk
Download or read book L'-superadditive Functions and Concepts of Multivariate Dependence written by Raymond Walter Falk and published by . This book was released on 1985 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multivariate Statistical Analysis by : Sam Kash Kachigan
Download or read book Multivariate Statistical Analysis written by Sam Kash Kachigan and published by . This book was released on 1991 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This classic book provides the much needed conceptual explanations of advanced computer-based multivariate data analysis techniques: correlation and regression analysis, factor analysis, discrimination analysis, cluster analysis, multi-dimensional scaling, perceptual mapping, and more. It closes the gap between spiraling technology and its intelligent application, fulfilling the potential of both.
Book Synopsis An Introduction to Copulas by : Roger B. Nelsen
Download or read book An Introduction to Copulas written by Roger B. Nelsen and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: Copulas are functions that join multivariate distribution functions to their one-dimensional margins. The study of copulas and their role in statistics is a new but vigorously growing field. In this book the student or practitioner of statistics and probability will find discussions of the fundamental properties of copulas and some of their primary applications. The applications include the study of dependence and measures of association, and the construction of families of bivariate distributions. With nearly a hundred examples and over 150 exercises, this book is suitable as a text or for self-study. The only prerequisite is an upper level undergraduate course in probability and mathematical statistics, although some familiarity with nonparametric statistics would be useful. Knowledge of measure-theoretic probability is not required. Roger B. Nelsen is Professor of Mathematics at Lewis & Clark College in Portland, Oregon. He is also the author of "Proofs Without Words: Exercises in Visual Thinking," published by the Mathematical Association of America.
Book Synopsis Multivariate Data Analysis by : Joseph Hair
Download or read book Multivariate Data Analysis written by Joseph Hair and published by Pearson Higher Ed. This book was released on 2016-08-18 with total page 816 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. For graduate and upper-level undergraduate marketing research courses. For over 30 years, Multivariate Data Analysis has provided readers with the information they need to understand and apply multivariate data analysis. Hair et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to readers how to understand and make use of the results of specific statistical techniques. In this Seventh Edition, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques.
Book Synopsis Multivariate Dependencies by : D.R. Cox
Download or read book Multivariate Dependencies written by D.R. Cox and published by CRC Press. This book was released on 2014-07-22 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large observational studies involving research questions that require the measurement of several features on each individual arise in many fields including the social and medical sciences. This book sets out both the general concepts and the more technical statistical issues involved in analysis and interpretation. Numerous illustrative examples ar
Book Synopsis Multivariate Dependence Using Nonparametric Copula Models by : Lu Lu
Download or read book Multivariate Dependence Using Nonparametric Copula Models written by Lu Lu and published by . This book was released on 2021 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dependence Modeling written by Harry Joe and published by World Scientific. This book was released on 2011 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1. Introduction : Dependence modeling / D. Kurowicka -- 2. Multivariate copulae / M. Fischer -- 3. Vines arise / R.M. Cooke, H. Joe and K. Aas -- 4. Sampling count variables with specified Pearson correlation : A comparison between a naive and a C-vine sampling approach / V. Erhardt and C. Czado -- 5. Micro correlations and tail dependence / R.M. Cooke, C. Kousky and H. Joe -- 6. The Copula information criterion and Its implications for the maximum pseudo-likelihood estimator / S. Gronneberg -- 7. Dependence comparisons of vine copulae with four or more variables / H. Joe -- 8. Tail dependence in vine copulae / H. Joe -- 9. Counting vines / O. Morales-Napoles -- 10. Regular vines : Generation algorithm and number of equivalence classes / H. Joe, R.M. Cooke and D. Kurowicka -- 11. Optimal truncation of vines / D. Kurowicka -- 12. Bayesian inference for D-vines : Estimation and model selection / C. Czado and A. Min -- 13. Analysis of Australian electricity loads using joint Bayesian inference of D-vines with autoregressive margins / C. Czado, F. Gartner and A. Min -- 14. Non-parametric Bayesian belief nets versus vines / A. Hanea -- 15. Modeling dependence between financial returns using pair-copula constructions / K. Aas and D. Berg -- 16. Dynamic D-vine model / A. Heinen and A. Valdesogo -- 17. Summary and future directions / D. Kurowicka
Book Synopsis Models for Dependent Time Series by : Granville Tunnicliffe Wilson
Download or read book Models for Dependent Time Series written by Granville Tunnicliffe Wilson and published by CRC Press. This book was released on 2015-07-29 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vect
Book Synopsis New Perspectives in Statistical Modeling and Data Analysis by : Salvatore Ingrassia
Download or read book New Perspectives in Statistical Modeling and Data Analysis written by Salvatore Ingrassia and published by Springer Science & Business Media. This book was released on 2011-06-29 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides recent research results in data analysis, classification and multivariate statistics and highlights perspectives for new scientific developments within these areas. Particular attention is devoted to methodological issues in clustering, statistical modeling and data mining. The volume also contains significant contributions to a wide range of applications such as finance, marketing, and social sciences. The papers in this volume were first presented at the 7th Conference of the Classification and Data Analysis Group (ClaDAG) of the Italian Statistical Society, held at the University of Catania, Italy.
Book Synopsis Dependence Modeling with Copulas by : Harry Joe
Download or read book Dependence Modeling with Copulas written by Harry Joe and published by CRC Press. This book was released on 2014-06-26 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured factor models that extend from the Gaussian assumption to copulas. It also discusses other multivariate constructions and parametric copula families that have different tail properties and presents extensive material on dependence and tail properties to assist in copula model selection. The author shows how numerical methods and algorithms for inference and simulation are important in high-dimensional copula applications. He presents the algorithms as pseudocode, illustrating their implementation for high-dimensional copula models. He also incorporates results to determine dependence and tail properties of multivariate distributions for future constructions of copula models.
Book Synopsis Copula-Based Markov Models for Time Series by : Li-Hsien Sun
Download or read book Copula-Based Markov Models for Time Series written by Li-Hsien Sun and published by Springer Nature. This book was released on 2020-07-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides statistical methodologies for time series data, focusing on copula-based Markov chain models for serially correlated time series. It also includes data examples from economics, engineering, finance, sport and other disciplines to illustrate the methods presented. An accessible textbook for students in the fields of economics, management, mathematics, statistics, and related fields wanting to gain insights into the statistical analysis of time series data using copulas, the book also features stand-alone chapters to appeal to researchers. As the subtitle suggests, the book highlights parametric models based on normal distribution, t-distribution, normal mixture distribution, Poisson distribution, and others. Presenting likelihood-based methods as the main statistical tools for fitting the models, the book details the development of computing techniques to find the maximum likelihood estimator. It also addresses statistical process control, as well as Bayesian and regression methods. Lastly, to help readers analyze their data, it provides computer codes (R codes) for most of the statistical methods.
