Sell your books and get cash! Enter to win $500 daily! Click here for more info.

Buy it Used or New Buy it New or Used

Other buying options Other buying options

Authorized Marketplace Sellers:
4 new & used from $92.81

Vector Integration and Stochastic Integration in Banach Spaces

Dinculeanu, Nicolae
ISBN-10: 0471377384
ISBN-13: 9780471377382

Our Price: $199.67
Free standard shipping
or $4.99 3-day shipping
In our Marketplace:
4 new & used from $92.81
"Vector Integration and Stochastic Integration in Banach Spaces goes far beyond the typical treatment of the scalar case given in other books on the subject. Along with such applications of the vector integration as the Reisz representation theorem and the Stieltjes integral for functions of one or two variables with finite semivariation, it explores the emergence of new classes of summable processes that make applications possible, including square integrable martingales in Hilbert spaces and processes with integrable variation or integrable semivariation in Banach spaces.
Numerous references to existing results supplement this exciting, breakthrough work."--BOOK JACKET. Title Summary field provided by Blackwell North America, Inc. All Rights ReservedPresents a new measure theoretic approach to stochastic integration, opening up the field for researchers in measure and integration theory, functional analysis, probability theory, and stochastic processes. First develops a general integration theory, discussing vector integration with respect to measures with finite semivariation, then applies the theory to stochastic integration in Banach spaces. Explores the emergence of new classes of summable processes that make applications possible, including square integrable martingales in Hilbert spaces and processes with integrable variation or semivariation in banach spaces. Dinculeanu teaches mathematics at the University of Florida-Gainesville. Annotation c. Book News, Inc., Portland, OR (booknews.com)Nicolae Dinculeanu, Ph.D., is Professor of Mathematics at the University of Florida in Gainesville.A breakthrough approach to the theory and applications of stochastic integration The theory of stochastic integration has become an intensely studied topic in recent years, owing to its extraordinarily successful application to financial mathematics, stochastic differential equations, and more. This book features a new measure theoretic approach to stochastic integration, opening up the field for researchers in measure and integration theory, functional analysis, probability theory, and stochastic processes. World-famous expert on vector and stochastic integration in Banach spaces Nicolae Dinculeanu compiles and consolidates information from disparate journal articles-including his own results-presenting a comprehensive, up-to-date treatment of the theory in two major parts. He first develops a general integration theory, discussing vector integration with respect to measures with finite semivariation, then applies the theory to stochastic integration in Banach spaces. Vector Integration and Stochastic Integration in Banach Spaces goes far beyond the typical treatment of the scalar case given in other books on the subject. Along with such applications of the vector integration as the Reisz representation theorem and the Stieltjes integral for functions of one or two variables with finite semivariation, it explores the emergence of new classes of summable processes that make applications possible, including square integrable martingales in Hilbert spaces and processes with integrable variation or integrable semivariation in Banach spaces. Numerous references to existing results supplement this exciting, breakthrough work.This volume presents the theory of stochastic integration at the highest level of generality, namely integration with respect to a process with finite semivariation and with values in a Banach space."...an important tool...gives the newest results in this field...shows an important application of vector integration..." (Bulletin of the Belgian Mathematical Society, Vol 11(1), 2004) "...it can be expected that...just like the author's 1967 volume, this book will stimulate further research on vector stochastic integration and can serve as a graduate-level reference work." (Mathematical Reviews Issue 2001h) "Dense, detailed, comprehensive introduction. Contains...material only found before in journals..." (American Mathematical Monthly, March 2002) "...a highly technical book." (The Mathematical Gazette, March 2002) "The author of this important and interesting book is a well-known specialist on vector measures." (Zentralblatt Math, Vol.974, No. 24 2001)
show more show less
Preface
Vector Integration
Preliminaries
Banach spaces
Classes of sets
Measurable functions
Simple measurability of operator-valued functions
Weak measurability
Integral of step functions
Totally measurable functions and the immediate integral
The Riesz representation theorem
The classical integral
The Bochner integral
Convergence theorems
Measures with finite variation
The variation of vector measures
Boundedness of [sigma]-additive measures
Variation of real-valued measures
Integration with respect to vector measures with finite variation
The indefinite integral
Integration with respect to gm
The Radon-Nikodym theorem
Conditional expectations
[sigma]-additive measures
[sigma]-additive measures on [sigma]-rings
Uniform [sigma]-additivity
Uniform absolute continuity and uniform [sigma]-additivity
Weak [sigma]-additivity
Uniform [sigma]-additivity of indefinite integrals
Weakly compact sets in L[superscript
1. subscript F] ([mu])
Measures with finite semivariation
The semivariation
Semivariation and norming spaces
The semivariation of [sigma]-additive measures
The family m[subscript F,Z] of measures
Integration with respect to a measure with finite semivariation
Measurability with respect to a vector measure
The seminorm m[subscript F,G](f)
The space of integrable functions
The integral
Convergence theorems
Properties of the space F[subscript D] (B, m[subscript F,G])
Relationship between the spaces F[subscript D](m)
The indefinite integral of measures with finite semivariation
Strong additivity
Extension of measures
Applications
The Riesz representation theorem
Integral representation of continuous linear operations on L[superscript p]-spaces
Random Gaussian measures
The Stochastic Integral
Summable processes
Notations
The measure I[subscript X]
Summable processes
Computation of I[subscript X] for predictable rectangles
Computation of I[subscript X] for stochastic intervals
The stochastic integral
The space F[subscript D] [characters not reproducible]
The integral [function of] HdI[subscript X]
A convergence theorem
The stochastic integral H - X
The stochastic integral and stopping times
Stochastic integral of elementary processes
Stopping the stochastic integral
Summability of stopped processes
The jumps of the stochastic integral
Convergence theorems
The completeness of the space L[superscript
1. subscript F,G](X)
The Uniform Convergence Theorem
The Vitali and the Lebesgue Convergence Theorems
The stochastic integral of [sigma]-elementary and of caglad processes as a pathwise Stieltjes integral
Summability of the stochastic integral
Summability criterion
Quasimartingales and the Doleans measure
The summability criterion
Local summability and local integrability
Definitions
Basic properties
Convergence theorems
Additional properties
Martingales
Stochastic integral of martingales
Square integrable martingales
Extension of the measure I[subscript M]
Summability of square integrable martingales
Properties of the space F[subscript F,G](M)
Isometrical isomorphism of L[superscript
1. subscript F,G](M) and L[superscript
2. subscript F]([mu subscript [M]])
Processes with Finite Variation
Functions with finite variation and their Stieltjes integral
Functions with finite variation
The variation function g
The measure associated to a function
The Stieltjes integral
Processes with finite variation
Definition and properties
Optional and predictable measures
The measure [mu subscript X]
Summability of processes with integrable variation
The stochastic integral as a Stieltjes integral
The pathwise stochastic integral
Semilocally summable processes
Processes with Finite Semivariation
Functions with finite semivariation and their Stieltjes integral
Functions with finite semivariation
Semivariation and norming spaces
The measure associated to a function
The Stieltjes integral with respect to a function with finite semivariation
Processes with finite semivariation
The semivariation process
The measure [mu]x
Summability of processes with integrable semivariation
The pathwise stochastic integral
Dual projections
Dual projection of measures
Dual projections of processes
Existence of dual projections
Processes with locally integrable variation or semivariation
Examples of processes with locally integrable variation or semivariation
Decomposition of local martingales
The Ito Formula
The Ito formula
Preliminary results
The vector quadratic variation
The quadratic variation
The process of jumps
Ito's formula
Stochastic Integration in the Plane
Preliminaries
Order relation in R[superscript 2]
The increment [Delta subscript zz], g
Right continuity
The filtration
The predictable [Sigma]-algebra
Stopping times
Stochastic processes
Extension of processes from R[superscript
2. subscript +] [times] [Omega] to R[superscript 2] [times] [Omega]
Summable processes
The measure I[subscript X]
Summable processes
The seminorm I[subscript X] and the space F[subscript F,G](X)
The integral [function of] HdI[subscript X]
The stochastic integral H - X
Properties of the stochastic integral
Convergence theorems
Extension of I[subscript X] to P([infinity])
Existence of left limits of X in L[superscript p subscript E]
Some properties of the integral [function of] HdI[subscript X]
Summability of stopped processes
Summability of the stochastic integral
Two-Parameter Martingales
Martingales
Square integrable martingales
A decomposition theorem
The measures [characters not reproducible] and [mu subscript [M]]
Summability of the square integrable martingales in Hilbert spaces
The space F[subscript F,G](I[subscript M])
Isometric isomorphism of L[superscript
1. subscript F,G](M) and L[superscript
2. subscript F]([mu subscript [M]])
Two-Parameter Processes with Finite Variation
Functions with finite variation in the plane
Monotone functions
Partitions
Variation corresponding to a partition
Variation of a function on a rectangle
Limits of the variation
The variation function g
Functions with finite variation
Functions vanishing outside a quadrant
Variation of real-valued functions
Lateral limits
Measures associated to functions
[sigma]-additivity of the measure m[subscript g]
The Stieltjes integral
Processes with finite variation
Processes with integrable variation
The measure [mu subscript X]
Summability of processes with integrable variation
The stochastic integral as a Stieltjes integral
Two-Parameter Processes with Finite Semivariation
Functions with finite semivariation in the plane
Functions with finite semivariation
Semivariation and norming spaces
The measure associated to a function
The Stieltjes integral for functions with finite semivariation in R[superscript 2]
Processes with finite semivariation in the plane
Processes with finite semivariation
The measure [mu subscript X]
Summability of processes with integrable semivariation
References


Edition: 2000
Publisher: John Wiley & Sons, Incorporated
Binding: Trade Cloth
Pages: 448
Size: 6.25" wide x 9.50" long x 1.00" tall
Weight: 1.69 lbs.
Language: English

100% Money Back Guarantee: Wrong item? No problem! Our hassle-free returns policy has you covered. We'll also process your order within 1-2 business days. Learn more about our shipping policy.