RT info:eu-repo/semantics/article
T1 Performance and estimation of the true error rate of classification rules built with additional information. An application to a cancer trial
A1 Conde del Río, David
A1 Salvador González, Bonifacio
A1 Rueda Sabater, María Cristina
A1 Fernández Temprano, Miguel Alejandro
K1 Bootstrap (Estadística)
AB Classification rules that incorporate additional information usually present in discrimination problems are receiving certain attention during the last years as they perform better than the usual rules in poor discrimination problems. Fern´andez et al (2006) proved that these rules have a lower unconditional misclassification probability than the usual Fisher’s rule but they did not consider the estimation of the conditional error probability when a training sample is given (the so-called true error rate) which is a very interesting parameter in practice.In this paper we consider the problem of estimating the true error rate of these classification rules in the classical topic of discrimination among two normal populations. We prove theoretical results on the apparent error rate of the rules that expose the need of new estimators of their true error rate. Our proposal is to also consider the additional information in the definition of the true error rate estimators. We propose four such new estimators. Two of them are defined incorporating the additional information into the leave-one-out-bootstrap. The other two are the corresponding cross-validation after bootstrap versions. We compare these new estimators with the usual ones in a simulation study and in a cancer trial application, showing the very good behavior, in terms of mean square error, of the leave-one-out bootstrap estimators that incorporate the available additional information.
PB De Gruyter
SN 1544-6115
YR 2013
FD 2013
LK http://uvadoc.uva.es/handle/10324/22932
UL http://uvadoc.uva.es/handle/10324/22932
LA eng
NO Statistical Applications in Genetics and Molecular Biology, 2013, 12(5), p. 583-602
NO Producción Científica
DS UVaDOC
RD 28-nov-2024