Two basic sources of error are associated to the use of bootstrap methods: one is derived from the fact that the true distribution is substituted by a suitable estimate, and the other is simulation errors. Some techniques to reduce or quantify these errors are discussed in this work. Some of them such as importance sampling or antithetic variates are adapted from classical Monte Carlo swindles, whereas others such as the centered and the balanced bootstrap, are more specific. The existence of common methodological trends, such as the use of influence functions and Von Mises expansions to estimate the variance of the methods is emphasized.
@article{urn:eudml:doc:40356,
title = {Efficient bootstrap simulation: an overview.},
journal = {Q\"uestii\'o},
volume = {14},
year = {1990},
pages = {43-88},
mrnumber = {MR1114050},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:40356}
}
Sánchez, Alex. Efficient bootstrap simulation: an overview.. Qüestiió, Tome 14 (1990) pp. 43-88. http://gdmltest.u-ga.fr/item/urn:eudml:doc:40356/