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Moment bounds of PH distributions with infinite or finite support based on the steepest increase property

Published online by Cambridge University Press:  22 July 2019

Qi-Ming He*
Affiliation:
University of Waterloo
Gábor Horváth*
Affiliation:
Budapest University of Technology and Economics
Illés Horváth*
Affiliation:
MTA-BME Information Systems Research Group
Miklós Telek*
Affiliation:
Budapest University of Technology and Economics
*
*Postal address: Department of Management Sciences, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada. Email address: q7he@uwaterloo.ca
**Postal address: Department of Networked Systems and Services, Budapest University of Technology and Economics, PO Box 91, 1521 Budapest, Hungary.
****Postal address: MTA-BME Information Systems Research Group, PO Box 91, 1521 Budapest, Hungary. Email address: horvath.illes.antal@gmail.com
**Postal address: Department of Networked Systems and Services, Budapest University of Technology and Economics, PO Box 91, 1521 Budapest, Hungary.

Abstract

The steepest increase property of phase-type (PH) distributions was first proposed in O’Cinneide (1999) and proved in O’Cinneide (1999) and Yao (2002), but since then has received little attention in the research community. In this work we demonstrate that the steepest increase property can be applied for proving previously unknown moment bounds of PH distributions with infinite or finite support. Of special interest are moment bounds free of specific PH representations except the size of the representation. For PH distributions with infinite support, it is shown that such a PH distribution is stochastically smaller than or equal to an Erlang distribution of the same size. For PH distributions with finite support, a class of distributions which was introduced and investigated in Ramaswami and Viswanath (2014), it is shown that the squared coefficient of variation of a PH distribution with finite support is greater than or equal to 1/(m(m + 2)), where m is the size of its PH representation.

Type
Original Article
Copyright
© Applied Probability Trust 2019 

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