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A Long Range Dependence Stable Process and an Infinite Variance Branching System

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Author Info
T. Bojdecki () (Institute of Mathematics, University of Warsaw)
Luis G. Gorostiza () (Departamento de Mathematicas, Centro de Investigacion y de Estudios Avanzados, LRSP)
A. Talarczyk () (Institute of Mathematics, University of Warsaw)
Abstract

We prove a functional limit theorem for the rescaled occupation time fluctuations of a (d, , )- branching particle system (particles moving in Rd according to a symmetric -stable L´evy process, branching law in the domain of attraction of a (1 + )-stable law, 0 < < 1, uniform Poisson initial state) in the case of intermediate dimensions, / < d < (1 + )/ . The limit is a process of the form K, where K is a constant,  is the Lebesgue measure on Rd, and  = (t)t0 is a (1+ )-stable process which has long range dependence. There are two long range dependence regimes, one for all > d/(d + ), which coincides with the case of finite variance branching ( = 1), and another one for  d/(d + ), where the long range dependence depends on the value of . The long range dependence is characterized by a dependence exponent  which describes the asymptotic behavior of the codi erence of increments of  on intervals far apart, and which is d/ for the first case and (1 + - d/(d + ))d/ for the second one. The convergence proofs use techniques of S0(Rd)-valued processes.

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File URL: http://www.repad.org/ca/on/lrsp/TRS425.pdf
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Publisher Info
Paper provided by Département des sciences administratives, UQO in its series RePAd Working Paper Series with number lrsp-TRS425.

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Length: 21 pages
Date of creation: 14 Nov 2005
Date of revision:
Handle: RePEc:pqs:wpaper:102006

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Related research
Keywords: Branching particle system; occupation time fluctuation; functional limit theorem; stable process; long range dependence.;

Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General
C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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This page was last updated on 2009-11-11.


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