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详细目录：
Contents
Preface xv
1 Introduction 1
1.1 Overview and background 2
1.2 Style and format 5
1.3 Notation, abbreviations and conventions 7
1.4 Conditions for multivariate distribution functions 11
1.4.1 Properties of a bivariate cdf F 11
1.4.2 Properties of a multivariate cdf F 11
1.5 Types of dependence 12
1.6 Copulas 12
1.7 View of statistical modelling 16
1.8 Bibliographic notes 17
1.9 Exercises 17
2 Basic concepts of dependence 19
2.1 Dependence properties and measures 20
2.2 Dependence orderings 35
2.3 Bibliographic notes 52
2.4 Exercises 53
2.5 Unsolved problems 56
3 Frechet classes 57
4 Construction of multivariate distributions 83
4.1 Desirable properties of a multivar~ate model 0 84
4.2 Laplace transforms and mixtures of powers 0 85
4.3 Mixtures of max-id distributions 98
4.4 Generalizations of functional forms 108
4.5 Mixtures of conditional distributions 111
4.5.1 Dependence properties'" 115
4.6 Convolution-closed infinitely divisible class 0 118
4.7 Multivariate distributions given bivariate margins 120
4.8 Molenberghs and Lesaffre construction 124
4.9 Spherically symmetric families: univariate margins 128
4.10 Other approaches 134
4.11 .Bibliographic notes 134
4.12 Exercises 135
4.13 Unsolved problems 138
5 Parametric families of copulas 139
5.1 Bivariate one-parameter families 0 139
5.2 Bivariate two-parameter families 149
5.3 Multivariate copulas with partial symmetry 155
5.4 Extensions to negati ve dependence '" 157
5.5 Multivariate copulas with general dependence 163
5.6 Bibliographic notes 166
5.7 Exercises 166
5.8 Unsolved problems 168
6 Multivariate extreme value distributions 169
6.1 Background: univariate extremes 169
6.2 Multivariate extreme value theory 172
6.2.1 Dependence properties 177
6.2.2 Extreme value limit results 179
6.3 Parametric families 182
6.3.1 Dependence families 182
6.3.2 Other parametric families 191
6.4 Point process approach • 194
6.5 Choice models 197
6.6 Mixtures of MEV distributions 202
6.7 Bibliographic notes 206
6.8 Exercises 207
6.9 Unsolved problems 208
7 Multivariate discrete distributions 209
7.1 Multivariate binary 209
7.2 Multivariate count 232
7.3 Multivariate models for ordinal responses 0 236
7.4 Multivariate models for nominal responses 231
7.5 Bibliographic notes 239
7.6 Exercises 240
7.7 Unsolved problems 242
8 Multivariate models with serial dependence 243
8.1 Markov chain models 244
8.2 k-dependent time series models 253
8.3 Latent variable models 258
8.4 Convolution-closed infinitely divisible class 259
8.5 Markov chains: dependence properties II< 210
8.6 Bibliographic notes 280
8.7 Exercises 281
8.8 Unsolved problems 282
9 Models from given conditional distributions 283
9.1 Conditional specifications and compatibility conditions 283
9.2 Examples 285
9.3 Bibliographic notes 296
9.4 Exercises
9.5 Unsolved problems
10 Statistical inference and computation 297
10.1 Estimation from likelihoods of margins 0 299
10.2 Extensions 311
10.3 Choice and comparison of models 316
10.4 Inference for Markov chains 317
10.5 Comments on Bayesian methods 319
10.6 Numerical methods 319
10.7 Bibliographic notes 321
10.8 Exercises 321
10.9 Unsolved problems 322
11 Data analysis and cOIllparison of Illodels 323
11.1 Example with multivariate binary response data 324
11.2 Example with multivariate ordinal response data 336
11.3 Example with multivariate extremes 344
11.4 Example with longitudinal binary data 352
11.5 Example with longitudinal count data 357
11.6 Example of inference for serially correlated data 369
11.7 Discussion 371
11.8 Exercises 372
Appendix 373
A.1 Laplace transforms 373
A.2 Other background results 377
A.2.1 Types of distribution functions and densities 377
A.2.2 Convex functions and inequalities 379
A.2.3 Maximum entropy 380
A.3 Bibliographic notes 382
References 383
Index 395
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