In animal breeding, Best Linear Unbiased Prediction, or BLUP, is a technique for estimating genetic merits. In general, it is a method of estimating random effects. It can be used to derive the Kalman filter, the method of Kriging used for ore reserve estimation, credibility theory used to work out insurance premiums, and Hoadley's quality measurement plan used to estimate a quality index. It can be used for removing noise from images and for small-area estimation. This paper presents the theory of BLUP, some examples of its application and its relevance to the foundations of statistics. Understanding of procedures for estimating random effects should help people to understand some complicated and controversial issues about fixed and random effects models and also help to bridge the apparent gulf between the Bayesian and Classical schools of thought.
Publié le : 1991-02-14
Classification:
Best linear unbiased predition (BLUP),
estimation of random effects,
fixed versus random effects,
foundations of statistics,
likelihood,
selection index,
Kalman filtering,
parametric empirical Bayes methods,
small-area estimation,
credibility theory,
ranking and selection
@article{1177011926,
author = {Robinson, G. K.},
title = {That BLUP is a Good Thing: The Estimation of Random Effects},
journal = {Statist. Sci.},
volume = {6},
number = {4},
year = {1991},
pages = { 15-32},
language = {en},
url = {http://dml.mathdoc.fr/item/1177011926}
}
Robinson, G. K. That BLUP is a Good Thing: The Estimation of Random Effects. Statist. Sci., Tome 6 (1991) no. 4, pp. 15-32. http://gdmltest.u-ga.fr/item/1177011926/