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.
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35 of 35 people found the following review helpful:
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very specialized topic,
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This review is from: Multivariate Models and Multivariate Dependence Concepts (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) (Hardcover)
Joe has written a book about multivariate dependencies. This is really a side topic to multivariate analysis which deals with concepts of dependence among variables. These dependencies lead to probability inequalities. This includes concepts like association, total positivity and majorization. These topics are important at times in proving statistical theorems particularly with regard to asymptotic theory. But if you do not specialize in such things you may not find it very interesting or useful. The new book by Marshall and Olkin deals with these methods in determining properties of lifetime distributions. Their earlier book on majorization is a classic and gives more comprehensive coverage of probability inequalities for multivariate data.
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Inside This Book
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First Sentence:
This book is devoted to multivariate models for non-normal response (e.g., binary, ordinal, count, extreme value), an area of probability and statistics with increasing activity and applications. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
tail dependence parameter, bivariate margins, cardiac surgery data, multivariate copulas, univariate margins, extreme value copulas, symmetric copulas, infinitely divisible class, bivariate copulas, upper tail dependence, specified logistic regression model, trivariate probit model, extreme value limit, binary rvs, rectangle inequality, positive dependence condition, exponential margins, multivariate extreme value distributions, independence copula, lower tail dependence, univariate likelihoods, copula family, positive serial dependence, multivariate probit model, proper cdf Key Phrases - Capitalized Phrases (CAPs): (learn more)
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
This book is devoted to multivariate models for non-normal response (e.g., binary, ordinal, count, extreme value), an area of probability and statistics with increasing activity and applications. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
tail dependence parameter, bivariate margins, cardiac surgery data, multivariate copulas, univariate margins, extreme value copulas, symmetric copulas, infinitely divisible class, bivariate copulas, upper tail dependence, specified logistic regression model, trivariate probit model, extreme value limit, binary rvs, rectangle inequality, positive dependence condition, exponential margins, multivariate extreme value distributions, independence copula, lower tail dependence, univariate likelihoods, copula family, positive serial dependence, multivariate probit model, proper cdf Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Model Log-lik New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 82
books:
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88
books
cite this book:
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- Subset Selection in Regression, Second Editon by Alan Miller in Front Matter (1), and Front Matter (2)
- Symmetric Multivariate and Related Distributions (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Kai-Tai Fang in Front Matter, and Back Matter
- The Analysis of Contingency Tables, Second Edition (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Brian S. Everitt in Front Matter (1), and Front Matter (2)
- Mathematical Statistics by Steven F. Arnold in Back Matter (1), and Back Matter (2)
- Design and Analysis of Cross-Over Trials (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by B. Jones in Front Matter (1), and Front Matter (2)
See all 82 books this book cites
- Modern Statistical And Mathematical Methods in Reliability (Quality, Reliability and Engineering Statistics) by Sallie Keller-McNulty on page 188, and page 321
- Extreme Values in Finance, Telecommunications, and the Environment (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Barbel Finkenstadt in Front Matter, and page 385
- System and Bayesian Reliability: Essays in Honor of Professor Richard E. Barlow (Series on Quality, Reliability and Engineering Statistics) by Yu Hayakawa on page 242, and page 256
- Distributions With Given Marginals and Statistical Modeling by Carles M. Cuadras on page 34, and page 112
- Modelling and Hedging Equity Derivatives by Nicholas Dunbar on page 26, and page 548
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