On moment-type estimators for a class of log-symmetric distributions

N. Balakrishnan; Helton Saulo; Marcelo Bourguignon; Xiaojun Zhu

doi:10.1007/s00180-017-0722-6

On moment-type estimators for a class of log-symmetric distributions

N. Balakrishnan
, Helton Saulo
, Marcelo Bourguignon^*
, Xiaojun Zhu

^*Corresponding author for this work

Department of Financial and Actuarial Mathematics

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	1339-1355
Number of pages	17
Journal	Computational Statistics
Volume	32
Issue number	4
DOIs	https://doi.org/10.1007/s00180-017-0722-6
Publication status	Published - 1 Dec 2017

Keywords

Asymptotic normality
Hodges–Lehmann estimator
Log-symmetric distributions
Maximum likelihood estimator
Modified moment estimator
Moment estimator

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10.1007/s00180-017-0722-6

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KW - Asymptotic normality

KW - Hodges–Lehmann estimator

KW - Log-symmetric distributions

KW - Maximum likelihood estimator

KW - Modified moment estimator

KW - Moment estimator

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On moment-type estimators for a class of log-symmetric distributions

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Keywords

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