Integrability of Stable Processes

Abstract

Let nu be a alpha-finite Borel measure on a separable metric space T, and let (X(t) t epsilon T) be a measurable alpha-stable process, O<a<2. Sample path integrals of a certain type arise in many situations, e.g. in multiple stochastic stable integration (Rosinski and Woyczyski 1987), in inversion formulae for the Fourier transform of stable noise (Cambanis 1988), in integral transformations between stationary and stationary increments stable processes (Cambanis and Maejima 1990) and others. It is important, therefore, to know exactly when the above integral is finite. Although much is known about this question, certain things appear to have been unknown in the case p<1 and even the known results are scattered in the literature and have never been put together, mainly because different cases have been handled using very different tools, varying from path order analysis to geometry of certain Banach spaces. As a result, researchers working with stable processes have had to justify in each case existence of sample path integrals (see Cambanis and Maejima (1990) for a recent example). It is our purpose in this paper, therefore, to give necessary and sufficient conditions for sample path integrability of stable processes in the case which has been to use form.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1990
Accession Number
ADA225959

Entities

People

  • Gennady Samorodnitsky

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Banach Space
  • Classification
  • Data Science
  • Differential Equations
  • Equations
  • Gaussian Processes
  • Hilbert Space
  • Information Science
  • Integrals
  • North Carolina
  • Path Integrals
  • Probability
  • Random Variables
  • Security
  • Statistics
  • Stochastic Processes
  • Universities

Fields of Study

  • Mathematics

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • Space