TY - JOUR
T1 - On moment-type estimators for a class of log-symmetric distributions
AU - Balakrishnan, N.
AU - Saulo, Helton
AU - Bourguignon, Marcelo
AU - Zhu, Xiaojun
N1 - Publisher Copyright:
© 2017, Springer-Verlag Berlin Heidelberg.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - In this paper, we propose three simple closed form estimators for a class of log-symmetric distributions on R+. The proposed methods make use of some key properties of this class of distributions. We derive the asymptotic distributions of these estimators. The performance of the proposed estimators are then compared with those of the maximum likelihood estimators through Monte Carlo simulations. Finally, some illustrative examples are presented to illustrate the methods of estimation developed here.
AB - In this paper, we propose three simple closed form estimators for a class of log-symmetric distributions on R+. The proposed methods make use of some key properties of this class of distributions. We derive the asymptotic distributions of these estimators. The performance of the proposed estimators are then compared with those of the maximum likelihood estimators through Monte Carlo simulations. Finally, some illustrative examples are presented to illustrate the methods of estimation developed here.
KW - Asymptotic normality
KW - Hodges–Lehmann estimator
KW - Log-symmetric distributions
KW - Maximum likelihood estimator
KW - Modified moment estimator
KW - Moment estimator
UR - http://www.scopus.com/inward/record.url?scp=85015696620&partnerID=8YFLogxK
U2 - 10.1007/s00180-017-0722-6
DO - 10.1007/s00180-017-0722-6
M3 - Article
AN - SCOPUS:85015696620
SN - 0943-4062
VL - 32
SP - 1339
EP - 1355
JO - Computational Statistics
JF - Computational Statistics
IS - 4
ER -