DocumentCode
2654875
Title
Fast estimation of uniformly-distributed random processes using extreme values
Author
Lever, Ken
Author_Institution
Commun. Res. Centre, Cardiff Univ., UK
Volume
2
fYear
2004
fDate
7-10 Nov. 2004
Firstpage
2324
Abstract
The nonlinear estimation, due to R. A. Fisher, of the mean of a uniformly-distributed random variable given by the average of the extreme values of N samples is unbiased. Furthermore, the variance of the estimation is very much less than that for the conventional linear estimation. Two extreme-based estimation of variance are considered, and their properties investigated for uniform, Gaussian and Laplacian distributions.
Keywords
Gaussian distribution; nonlinear estimation; random processes; sampling methods; Gaussian distributions; Laplacian distributions; nonlinear estimation; uniformly-distributed random processes; Estimation theory; Gaussian approximation; Laplace equations; Neural networks; Probability distribution; Random processes; Random variables;
fLanguage
English
Publisher
ieee
Conference_Titel
Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on
Print_ISBN
0-7803-8622-1
Type
conf
DOI
10.1109/ACSSC.2004.1399583
Filename
1399583
Link To Document