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General Stochastic Measures de Vadym M Radchenko

General Stochastic Measures

Autor Vadym M Radchenko
en Limba Engleză Hardback – 29 aug 2022
This book is devoted to the study of stochastic measures (SMs). An SM is a sigma-additive in probability random function, defined on a sigma-algebra of sets. SMs can be generated by the increments of random processes from many important classes such as square-integrable martingales and fractional Brownian motion, as well as alpha-stable processes. SMs include many well-known stochastic integrators as partial cases. General Stochastic Measures provides a comprehensive theoretical overview of SMs, including the basic properties of the integrals of real functions with respect to SMs. A number of results concerning the Besov regularity of SMs are presented, along with equations driven by SMs, types of solution approximation and the averaging principle. Integrals in the Hilbert space and symmetric integrals of random functions are also addressed. The results from this book are applicable to a wide range of stochastic processes, making it a useful reference text for researchers and postgraduate or postdoctoral students who specialize in stochastic analysis.
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Specificații

ISBN-13: 9781786308283
ISBN-10: 1786308282
Pagini: 272
Dimensiuni: 161 x 240 x 19 mm
Greutate: 0.57 kg
Editura: Wiley
Locul publicării:Hoboken, United States

Cuprins

Abbreviations and Notations ix
Introduction xi
Chapter 1 Integration with Respect to Stochastic Measures 1
1.1. Preliminaries 1
1.2. Stochastic measures 2
1.2.1. Definition and examples of SMs 2
1.2.2. Convergence defined by an SM 5
1.3. Integration of deterministic functions 6
1.4. Limit theorems for integral of deterministic functions 11
1.4.1 Convergence of ¿A fn d¿ 13
1.4.2 Convergence of ¿X  fd¿n 14
1.5. ¿-finite stochastic measures 16
1.6. Riemann integral of a random function w.r.t. a deterministic measure 21
1.6.1. Definition of the integral 21
1.6.2. Interchange of the order of integration 27
1.6.3. Iterated integral and integration by parts 29
1.7. Exercises 32
1.8. Bibliographical notes 34
Chapter 2 Path Properties of Stochastic Measures 35
2.1. Sample functions of stochastic measures and Besov spaces 35
2.1.1. Besov spaces 35
2.1.2. Auxiliary lemmas 37
2.1.3 Stochastic measures on [0, 1] 42
2.1.4 Stochastic measures on [0, 1] d 44
2.2. Fourier series expansion of stochastic measures 46
2.2.1 Convergence of Fourier series of the process ¿(t) 46
2.2.2. Convergence of stochastic integrals 49
2.3. Continuity of the integral 51
2.3.1. Estimate of an integral 51
2.3.2. Parameter dependent integral 54
2.3.3. Continuity with respect to the upper limit 55
2.4. Exercises 57
2.5. Bibliographical notes 59
Chapter 3 Equations Driven by Stochastic Measures 61
3.1 Parabolic equation in R (case d¿¿ (x)) 61
3.1.1. Problem and the main result 61
3.1.2 Lemma About the Hölder Continuity in X 70
3.1.3 Lemma about the Hölder continuity in t 75
3.2 Heat equation in Rd (case d¿(t)) 78
3.2.1. Additional estimate of an integral 78
3.2.2. Problem and the main result 80
3.2.3 Lemma About the Hölder Continuity in X 84
3.2.4 Lemma about the Hölder continuity in t 91
3.3 Wave equation in R (case d¿(x)) 99
3.3.1. Problem and the main result 99
3.3.2 Lemma About the Hölder Continuity in X 102
3.3.3 Lemma about the Hölder continuity in t 106
3.4 Wave equation in R (case d¿(t)) 108
3.4.1. Problem and the main result 108
3.4.2 Lemma About the Lipschiz Continuity in X 109
3.4.3 Lemma about the Hölder continuity in t 111
3.5 Parabolic evolution equation in R d (weak solution, case d¿(t)) 114
3.6. Exercises 119
3.7. Bibliographical notes 120
Chapter 4 Approximation of Solutions of the Equations 123
4.1 Parabolic equation in R (case d¿(x))  123
4.1.1. Problem and the main result 123
4.1.2. Auxiliary lemmas 130
4.1.3. Examples 133
4.2 Heat equation in Rd (case d¿(t)) 135
4.2.1. Problem and the main result 135
4.2.2. Auxiliary lemma 138
4.2.3. Examples 139
4.3 Wave equation in R (case d¿(t)) 140
4.3.1. Approximation by using the convergence of paths of SMs 140
4.3.2. Approximation by using the Fourier partial sums 142
4.3.3. Approximation by using the Fejèr sums 149
4.3.4. Auxiliary lemma 151
4.3.5. Example 153
4.4. Exercises 154
4.5. Bibliographical notes 155
Chapter 5. Integration and Evolution Equations in Hilbert Spaces 157
5.1. Preliminaries 157
5.2. Equations and integral with a real-valued SM 160
5.2.1. Integral w.r.t. a real-valued SM 160
5.2.2. Evolution equations driven by a real-valued SM 163
5.3. Equations and integrals with a Hilbert space-valued SM 166
5.3.1. Integrals w.r.t. a U-valued SM 166
5.3.2. Evolution equations driven by a U-valued SM 171
5.4. Exercises 172
5.5. Bibliographical notes 173
Chapter 6 Symmetric Integrals 175
6.1. Introduction 175
6.2. SM has finite strong cubic variation 176
6.3. Stratonovich-type integral 177
6.4. SDE driven by an SM 181
6.5. Wong-Zakai approximation 183
6.6. Some counterexamples 188
6.7. Exercises 192
6.8. Bibliographical notes 193
Chapter 7 Averaging Principle 195
7.1. Heat equation 195
7.1.1. Introduction 195
7.1.2. The problem 196
7.1.3. Averaging principle 197
7.2. Equation with the symmetric integral 202
7.2.1. Introduction 202
7.2.2. Averaging principle 205
7.3. Exercises 211
7.4. Bibliographical notes 212
Chapter 8 Solutions to Exercises 213
References 231
Index 241