Open Access
Translator Disclaimer
November, 1995 Asymptotic Analysis of Tail Probabilities Based on the Computation of Moments
Joseph Abate, Gagan L. Choudhury, David M. Lucantoni, Ward Whitt
Ann. Appl. Probab. 5(4): 983-1007 (November, 1995). DOI: 10.1214/aoap/1177004603


Choudhury and Lucantoni recently developed an algorithm for calculating moments of a probability distribution by numerically inverting its moment generating function. They also showed that high-order moments can be used to calculate asymptotic parameters of the complementary cumulative distribution function when an asymptotic form is assumed, such as $F^c(x) \sim \alpha x^\beta e^{-\eta x}$ as $x \rightarrow \infty$. Moment-based algorithms for computing asymptotic parameters are especially useful when the transforms are not available explicitly as in models of busy periods or polling systems. Here we provide additional theoretical support for this moment-based algorithm for computing asymptotic parameters and new refined estimators for the case $\beta \neq 0$. The new refined estimators converge much faster (as a function of moment order) than the previous estimators, which means that fewer moments are needed, thereby speeding up the algorithm. We also show how to compute all the parameters in a multiterm asymptote of the form $F^c(x) \sim \sum^m_{k = 1} \alpha_k x^{\beta - k + 1} e^{-\eta x}$. We identify conditions under which the estimators converge to the asymptotic parameters and we determine rates of convergence, focusing especially on the case $\beta \neq 0$. Even when $\beta = 0$, we show that it is necessary to assume the asymptotic form for the complementary distribution function; the asymptotic form is not implied by convergence of the moment-based estimators alone. In order to get good estimators of the asymptotic decay rate $\eta$ and the asymptotic power $\beta$ when $\beta \neq 0$, a multiple-term asymptotic expansion is required. Such asymptotic expansions typically hold when $\beta \neq 0$, corresponding to the dominant singularity of the transform being a multiple pole ($\beta$ a positive integer) or an algebraic singularity (branch point, $\beta$ noninteger). We also show how to modify the moment generating function in order to calculate asymptotic parameters when all moments do not exist (the case $\eta = 0)$.


Download Citation

Joseph Abate. Gagan L. Choudhury. David M. Lucantoni. Ward Whitt. "Asymptotic Analysis of Tail Probabilities Based on the Computation of Moments." Ann. Appl. Probab. 5 (4) 983 - 1007, November, 1995.


Published: November, 1995
First available in Project Euclid: 19 April 2007

zbMATH: 0865.65106
MathSciNet: MR1384363
Digital Object Identifier: 10.1214/aoap/1177004603

Primary: 65D15
Secondary: 60E10 , 65B05

Keywords: asymptotics , extrapolation , moment generating functions , moments , Numerical transform inversion , tail probabilities

Rights: Copyright © 1995 Institute of Mathematical Statistics


Vol.5 • No. 4 • November, 1995
Back to Top