Multivariate Models and Multivariate Dependence Concepts

Harry Joe

May 1, 1997 by Chapman and Hall/CRC
Reference - 424 Pages
ISBN 9780412073311 - CAT# C7331
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

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Features

oConstruction of multivariate models to cover various types of dependence structures
  • Use of stochastic representations, mixtures, and latent variables to construct parametric models with desirable dependence properties
  • Use of the copula as a summary of the dependence in a multivariate distribution - independent of the univariate margins
  • Frechet classes with given marginal distributions
  • Time series models with given univariate margins
  • Emphasis on properties of multivariate models to decide their applicability
  • Estimation method consisting of parameter estimates from likelihoods of marginal distributions of a multivariate model - together with the jackknife methods for standard error estimates
  • Comparison of models in data analysis examples, including multivariate and longitudinal response data
  • Summary

    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.