|
Activity Number:
|
499
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Nonparametric Statistics
|
| Abstract - #309291 |
|
Title:
|
Optimal Ranked Set Sampling Estimation Based on Medians from Multiple Set Sizes
|
|
Author(s):
|
Nader Gemayel*+ and Elizabeth A. Stasny and Douglas Wolfe
|
|
Companies:
|
The Ohio State University and The Ohio State University and The Ohio State University
|
|
Address:
|
404 Cockins Hall, Columbus, OH, 43210,
|
|
Keywords:
|
Best linear unbiased estimator (BLUE( ; Beta distribution ; Lagrange multipliers ; order statistics ; robust estimation ; Taylor's theorem
|
|
Abstract:
|
Ranked set sampling (RSS) is a data collection technique that makes use of expert knowledge to rank sample units before measuring them. Even though rankings are not always perfect, RSS is useful in situations when obtaining measurements is costly, difficult, or destructive. Research in this area has tended to focus on the case of balanced RSS, in which all set sizes are equal and exactly one observation of each rank is measured in each cycle. This article represents a departure from balanced RSS when we encounter different set sizes within a single sample. More specifically, we propose a distribution-free estimator for the median of a symmetric distribution using medians of ranked set samples of various set sizes from this distribution. This estimator is seen to be robust over a wide class of symmetric distributions.
|
