## Multivariate Models and Multivariate Dependence ConceptsThis 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. |

### What people are saying - Write a review

User Review - Flag as inappropriate

Perfect book！！！

### Contents

Basic concepts of dependence | 19 |

Frechet classes | 57 |

Construction of multivariate distributions | 83 |

Parametric families of copulas | 139 |

Multivariate extreme value distributions | 169 |

Multivariate discrete distributions | 209 |

Multivariate models with serial dependence | 243 |

Models from given conditional distributions | 283 |

Statistical inference and computation | 297 |

Data analysis and comparison of models | 323 |

Appendix | 373 |

383 | |

395 | |

### Common terms and phrases

analysis asymptotic Bernoulli distribution bivariate copulas bivariate distribution bivariate margins compatibility concordance ordering conditional distributions correlation covariance matrix decreasing denoted dependence concepts dependence structure derivatives estimates example exponential family extreme value limit family B6 family of copulas Frechet lower bound Frechet upper bound Hence increases in concordance independent inequality inference infinitely divisible Kendall's tau likelihood log-likelihoods logistic lower tail dependence m-variate marginal distributions Markov chain max-id maximum entropy MEV distributions mixture multivariate binary multivariate distributions multivariate extension multivariate models multivariate probit multivariate probit model negative dependence non-negative numerical obtained parametric families Poisson Poisson distribution positive dependence Pr(Y probability probit model proof random vector range of dependence regression Section 5.1 sequence stationary statistical stochastic representation subsection Suppose survival function symmetric Table tail dependence parameter Theorem tion trivariate univariate univariate cdf univariate parameters upper tail dependence

### References to this book

Comparison Methods for Stochastic Models and Risks Alfred Müller,Dietrich Stoyan No preview available - 2002 |