Simple fractions and linear decomposition of some convolutions of measures

Jolanta K. Misiewicz; Roger Cooke

Discussiones Mathematicae Probability and Statistics (2001)

  • Volume: 21, Issue: 2, page 149-157
  • ISSN: 1509-9423

Abstract

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Every characteristic function φ can be written in the following way: φ(ξ) = 1/(h(ξ) + 1), where h(ξ) = ⎧ 1/φ(ξ) - 1 if φ(ξ) ≠ 0 ⎨ ⎩ ∞ if φ(ξ) = 0 This simple remark implies that every characteristic function can be treated as a simple fraction of the function h(ξ). In the paper, we consider a class C(φ) of all characteristic functions of the form φ a ( ξ ) = [ a / ( h ( ξ ) + a ) ] , where φ(ξ) is a fixed characteristic function. Using the well known theorem on simple fraction decomposition of rational functions we obtain that convolutions of measures μ a with μ ̂ a ( ξ ) = φ a ( ξ ) are linear combinations of powers of such measures. This can simplify calculations. It is interesting that this simplification uses signed measures since coefficients of linear combinations can be negative numbers. All the results of this paper except Proposition 1 remain true if we replace probability measures with complex valued measures with finite variation, and replace the characteristic function with Fourier transform.

How to cite

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Jolanta K. Misiewicz, and Roger Cooke. "Simple fractions and linear decomposition of some convolutions of measures." Discussiones Mathematicae Probability and Statistics 21.2 (2001): 149-157. <http://eudml.org/doc/287613>.

@article{JolantaK2001,
abstract = {Every characteristic function φ can be written in the following way: φ(ξ) = 1/(h(ξ) + 1), where h(ξ) = ⎧ 1/φ(ξ) - 1 if φ(ξ) ≠ 0 ⎨ ⎩ ∞ if φ(ξ) = 0 This simple remark implies that every characteristic function can be treated as a simple fraction of the function h(ξ). In the paper, we consider a class C(φ) of all characteristic functions of the form $φ_\{a\}(ξ) = [a/(h(ξ) + a)]$, where φ(ξ) is a fixed characteristic function. Using the well known theorem on simple fraction decomposition of rational functions we obtain that convolutions of measures $μ_\{a\}$ with $μ̂_\{a\}(ξ) = φ_\{a\}(ξ)$ are linear combinations of powers of such measures. This can simplify calculations. It is interesting that this simplification uses signed measures since coefficients of linear combinations can be negative numbers. All the results of this paper except Proposition 1 remain true if we replace probability measures with complex valued measures with finite variation, and replace the characteristic function with Fourier transform.},
author = {Jolanta K. Misiewicz, Roger Cooke},
journal = {Discussiones Mathematicae Probability and Statistics},
keywords = {measure; convolution of measures; characteristic function; simple fraction},
language = {eng},
number = {2},
pages = {149-157},
title = {Simple fractions and linear decomposition of some convolutions of measures},
url = {http://eudml.org/doc/287613},
volume = {21},
year = {2001},
}

TY - JOUR
AU - Jolanta K. Misiewicz
AU - Roger Cooke
TI - Simple fractions and linear decomposition of some convolutions of measures
JO - Discussiones Mathematicae Probability and Statistics
PY - 2001
VL - 21
IS - 2
SP - 149
EP - 157
AB - Every characteristic function φ can be written in the following way: φ(ξ) = 1/(h(ξ) + 1), where h(ξ) = ⎧ 1/φ(ξ) - 1 if φ(ξ) ≠ 0 ⎨ ⎩ ∞ if φ(ξ) = 0 This simple remark implies that every characteristic function can be treated as a simple fraction of the function h(ξ). In the paper, we consider a class C(φ) of all characteristic functions of the form $φ_{a}(ξ) = [a/(h(ξ) + a)]$, where φ(ξ) is a fixed characteristic function. Using the well known theorem on simple fraction decomposition of rational functions we obtain that convolutions of measures $μ_{a}$ with $μ̂_{a}(ξ) = φ_{a}(ξ)$ are linear combinations of powers of such measures. This can simplify calculations. It is interesting that this simplification uses signed measures since coefficients of linear combinations can be negative numbers. All the results of this paper except Proposition 1 remain true if we replace probability measures with complex valued measures with finite variation, and replace the characteristic function with Fourier transform.
LA - eng
KW - measure; convolution of measures; characteristic function; simple fraction
UR - http://eudml.org/doc/287613
ER -

References

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  1. [1] W. Feller, An Introduction to Probability Theory and its Application, volume II. Wiley, New York 1966. Zbl0138.10207
  2. [2] I.S. Gradshteyn and I.M. Ryzhik, Table of Integrals, Series and Products, Fifth Edition, Academic Press 1997. Zbl0918.65002
  3. [3] N. Jacobson, Basic Algebra I, W.H. Freeman and Company, San Francisco 1974. 
  4. [4] S. Lang, Algebra, Addison-Weslay 1970, Reading USA, Second Edition. 

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