Language:   Search:   Contact
World of
Mathematics
Database
»ZBMATH«
MSC 2000
MSC 2010
Reviewer
Service
Subscription
»ZBMATH«
ZBMATH Database | Advanced Search Print
Read more | Try MathML | Hide
Zentralblatt MATH has released its new interface!
For an improved author identification, see the new author database of ZBMATH.

ZBMATH Database Simple Search Advanced Search Command Search

Advanced Search

Query:
Fill in the form and click »Search«...
Format:
Display: entries per page entries
Zbl 1065.60042
Meerschaert, Mark M.; Scheffler, Hans-Peter
Limit theorems for continuous-time random walks with infinite mean waiting times.
(English)
[J] J. Appl. Probab. 41, No. 3, 623-638 (2004). ISSN 0021-9002

Let $\{D(x), x\geq 0\}$ be a strictly stable subordinator with index $\beta \in (0,1)$, and $\{E(t), t\geq 0\}$ be the corresponding hitting time process. The authors prove (Proposition 3.1) that the process $E(t)$ is self-similar with exponent $\beta$. They also point out (Corollaries 3.1--3.3, Remark 3.1) some distributional and moment properties of $E(t)$ and verify that $E(t)$ has neither stationary nor independent increments. Let $\{N(t), t\geq 0\}$ be a standard renewal process whose inter-arrival time belongs to the domain of attraction of a positive strictly stable law; $Y_1, Y_2,\ldots$ be i.i.d. random vectors, which are independent of $N(t)$. The process $X_t:=Y_1+\ldots+Y_{N(t)}, t\geq 0$, is called a continuous-time random walk. Let $A(t)$ be an operator Lévy motion. The authors prove (Theorem 3.2 and Corollary 3.4) that all finite-dimensional distributions of properly scaled and normalized process $N(t)$ converge to finite-dimensional distributions of $E(t)$. The convergence is also established in the Skorokhod space equipped with the $J_1$-topology. Theorem 4.2 contains a similar result for $X_t$. There the Skorokhod space is equipped with the $M_1$-topology, and the limiting process is $\{M(t):=A(E(t)), t\geq 0\}$. Further (Corollaries 4.1--4.3, Theorem 4.3) the authors investigate properties of the process $M(t)$. In particular, they check that the process is operator self-similar with exponent $\beta E$, where $E$ is a matrix with real entries, but not operator stable; it does not have stationary increments. Finally, Theorem 5.1 states that the density of $M(t)$ solves a fractional kinetic equation which is a generalization of a fractional partial differential equation for Hamiltonian chaos.
[Aleksander Iksanov (Kiev)]
MSC 2000:
*60G50 Sums of independent random variables
60K40 Physical appl. of random processes

Keywords: operator self-similar process; continuous-time random walk

Cited in: Zbl 1186.60079 Zbl 1147.60025

Login Username: Password:

Highlights
Scientific prize winners of the ICM 2010
Overhang
Lie groups, physics and geometry. An introduction for physicists, engineers and chemists.

Master Server

Zentralblatt MATH Berlin [Germany]

© FIZ Karlsruhe GmbH

Zentralblatt MATH master server is maintained by the Editorial Office in Berlin, Section Mathematics and Computer Science of FIZ Karlsruhe and is updated daily.

Other Mirror Sites



Copyright © 2013 Zentralblatt MATH | European Mathematical Society | FIZ Karlsruhe | Heidelberg Academy of Sciences
Published by Springer-Verlag | Webmaster