RT Journal Article
T1 Minimum ϕ-Divergence Estimation in Constrained Latent Class Models for Binary Data
A1 Felipe Ortega, Ángel
A1 Miranda Menéndez, Pedro
A1 Pardo Llorente, Leandro
AB The main purpose of this paper is to introduce and study the behavior of minimum (Formula presented.)-divergence estimators as an alternative to the maximum-likelihood estimator in latent class models for binary items. As it will become clear below, minimum (Formula presented.)-divergence estimators are a natural extension of the maximum-likelihood estimator. The asymptotic properties of minimum (Formula presented.)-divergence estimators for latent class models for binary data are developed. Finally, to compare the efficiency and robustness of these new estimators with that obtained through maximum likelihood when the sample size is not big enough to apply the asymptotic results, we have carried out a simulation study.
PB Springer
SN 0033-3123
YR 2015
FD 2015
LK https://hdl.handle.net/20.500.14352/34635
UL https://hdl.handle.net/20.500.14352/34635
LA eng
NO MECD
NO MICINN
DS Docta Complutense
RD 21 ago 2024