Limit Theorems for Stochastic Processes

Couverture
Springer Science & Business Media, 9 mars 2013 - 664 pages
Initially the theory of convergence in law of stochastic processes was developed quite independently from the theory of martingales, semimartingales and stochastic integrals. Apart from a few exceptions essentially concerning diffusion processes, it is only recently that the relation between the two theories has been thoroughly studied. The authors of this Grundlehren volume, two of the international leaders in the field, propose a systematic exposition of convergence in law for stochastic processes, from the point of view of semimartingale theory, with emphasis on results that are useful for mathematical theory and mathematical statistics. This leads them to develop in detail some particularly useful parts of the general theory of stochastic processes, such as martingale problems, and absolute continuity or contiguity results. The book contains an introduction to the theory of martingales and semimartingales, random measures stochastic integrales, Skorokhod topology, etc., as well as a large number of results which have never appeared in book form, and some entirely new results. The second edition contains some additions to the text and references. Some parts are completely rewritten.
 

Table des matières

The General Theory of Stochastic Processes
1
Convergence to a Mixture of PIIs Stable Convergence
5
1d The Localization Procedure
8
2e The Discrete Case
25
Semimartingales and Stochastic Integrals
38
4e Quadratic Variation of a Semimartingale and Itos Formula
51
4f DoléansDade Exponential Formula
58
Characteristics of Semimartingales
75
The General Case
362
Convergence Quadratic Variation Stochastic Integrals
376
Convergence of Processes with Independent Increments
389
Functional Convergence and Characteristics
413
4c Another Necessary and Sufficient Condition for Functional
418
3d Sufficient Condition for Convergence
424
5c FiniteDimensional Convergence of PIIs to a Gaussian
450
Convergence to a Process with Independent Increments
456

2c A Canonical Representation for Semimartingales
84
Some Examples
91
Semimartingales with Independent Increments
101
Processes with Independent Increments
114
Processes with Conditionally Independent Increments
124
Semimartingales Stochastic Exponential and Stochastic Logarithm
134
Martingale Problems and Changes of Measures
142
Martingale Problems and Semimartingales
151
Absolutely Continuous Changes of Measures
165
Representation Theorem for Martingales
179
Integrals of VectorValued Processes and omartingales
203
Laplace Cumulant Processes and Esschers Change of Measures
219
Hellinger Processes Absolute Continuity
227
Predictable Criteria for Absolute Continuity and Singularity
245
Hellinger Processes for Solutions of Martingale Problems
254
3c The Case Where Local Uniqueness Holds
266
Examples
272
Contiguity Entire Separation Convergence in Variation
284
Predictable Criteria for Contiguity and Entire Separation
291
Examples
304
Skorokhod Topology and Convergence of Processes
324
Continuity for the Skorokhod Topology
337
Weak Convergence
347
The QuasiLeft Continuous Case
355
Necessary and Sufficient Conditions
470
3c Central Limit Theorem for Triangular Arrays
477
3f Limit Theorems for Functionals of Markov Processes
486
Vanishes Almost Nowhere
501
and Mixing Convergence
506
5c Stable Convergence
512
5d Mixing Convergence
518
Identification of the Limit
527
2c Identification of the Limit Via Convergence
533
Limit Theorems for Semimartingales
540
Applications
554
Convergence of Stochastic Integrals
564
Stability for Stochastic Differential Equation
575
Stable Convergence to a Progressive Conditional Continuous PII
583
Limit Theorems Density Processes and Contiguity
592
Convergence of the LogLikelihood to a Process
612
The Statistical Invariance Principle
620
Bibliographical Comments
629
142
640
References
641
Index of Symbols 653
652
557
658
501
659
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