Channel coding considerations for wireless LANs
Daniel J. Costello Jr., Pierre R. Chevillat, et al.
ISIT 1997
Trimmed L-moments, defined by Elamir and Seheult [2003. Trimmed L-moments. Comput. Statist. Data Anal. 43, 299-314], summarize the shape of probability distributions or data samples in a way that remains viable for heavy-tailed distributions, even those for which the mean may not exist. We derive some further theoretical results concerning trimmed L-moments: a relation with the expansion of the quantile function as a weighted sum of Jacobi polynomials; the bounds that must be satisfied by trimmed L-moments; recurrences between trimmed L-moments with different degrees of trimming; and the asymptotic distributions of sample estimators of trimmed L-moments. We also give examples of how trimmed L-moments can be used, analogously to L-moments, in the analysis of heavy-tailed data. Examples include identification of distributions using a trimmed L-moment ratio diagram, shape parameter estimation for the generalized Pareto distribution, and fitting generalized Pareto distributions to a heavy-tailed data sample of computer network traffic. © 2007 Elsevier B.V. All rights reserved.
Daniel J. Costello Jr., Pierre R. Chevillat, et al.
ISIT 1997
A. Gupta, R. Gross, et al.
SPIE Advances in Semiconductors and Superconductors 1990
Jaione Tirapu Azpiroz, Alan E. Rosenbluth, et al.
SPIE Photomask Technology + EUV Lithography 2009
David L. Shealy, John A. Hoffnagle
SPIE Optical Engineering + Applications 2007