Premium
Robustness of Selection Procedures
Author(s) -
Domröse H.,
Rasch D.
Publication year - 1987
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710290504
Subject(s) - robustness (evolution) , quantile , estimator , mathematics , sample mean and sample covariance , statistics , sample size determination , truncated mean , selection (genetic algorithm) , normal distribution , mathematical optimization , computer science , artificial intelligence , biochemistry , chemistry , gene
In the article Bechhofers Indifference‐zone formulation for selecting the t populations with the t highest means is considered in a set of non‐normal distributions. Selection rules based on the sample mean, the 10% and the 20% trimmed means, two estimators proposed by Tiku (1981) for valuating the smallest and highest accepted sample values higher, the sample median and a linear combination of quantile estimators, two adaptive procedures and a ranksum procedure are investigated in a large scale simulation experiment in respect of their robustness against deviations from an assumed distribution. Robustness is understood as a small percentage of the difference β A ‐β between the actual probability of incorrect selection β A and the nominal β‐value. We obtained a relatively good robustness for the classical sample mean selection rule and useful derivations for the employment of other selection rules in an area of practical importance.