Closed-form estimators for the inverse Nakagami distribution

Abstract The inverse Nakagami distribution due to Louzada et al. (2018) does not have closed-form maximum likelihood estimators. Closed-form estimators by adapting the method of moments are proposed in this note. Also proposed is a bias corrected version of the estimators. Large sample properties in...

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Main Authors: VICTOR NAWA, SARALEES NADARAJAH
Format: Article
Language:English
Published: Academia Brasileira de Ciências 2025-03-01
Series:Anais da Academia Brasileira de Ciências
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Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652025000100401&tlng=en
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author VICTOR NAWA
SARALEES NADARAJAH
author_facet VICTOR NAWA
SARALEES NADARAJAH
author_sort VICTOR NAWA
collection DOAJ
description Abstract The inverse Nakagami distribution due to Louzada et al. (2018) does not have closed-form maximum likelihood estimators. Closed-form estimators by adapting the method of moments are proposed in this note. Also proposed is a bias corrected version of the estimators. Large sample properties including asymptotic variances of the proposed estimators are derived. A simulation study and data applications are provided to compare the performances of the maximum likelihood estimators, the proposed estimators and their bias corrected versions.
format Article
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institution Kabale University
issn 1678-2690
language English
publishDate 2025-03-01
publisher Academia Brasileira de Ciências
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series Anais da Academia Brasileira de Ciências
spelling doaj-art-d542aa33e3cc4d5aa1f502fdfcc7af422025-08-20T03:44:35ZengAcademia Brasileira de CiênciasAnais da Academia Brasileira de Ciências1678-26902025-03-0197110.1590/0001-3765202520240838Closed-form estimators for the inverse Nakagami distributionVICTOR NAWAhttps://orcid.org/0000-0001-6019-7325SARALEES NADARAJAHhttps://orcid.org/0000-0002-0481-0372Abstract The inverse Nakagami distribution due to Louzada et al. (2018) does not have closed-form maximum likelihood estimators. Closed-form estimators by adapting the method of moments are proposed in this note. Also proposed is a bias corrected version of the estimators. Large sample properties including asymptotic variances of the proposed estimators are derived. A simulation study and data applications are provided to compare the performances of the maximum likelihood estimators, the proposed estimators and their bias corrected versions.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652025000100401&tlng=endigamma functionmethod of momentstrigamma functionvariance
spellingShingle VICTOR NAWA
SARALEES NADARAJAH
Closed-form estimators for the inverse Nakagami distribution
Anais da Academia Brasileira de Ciências
digamma function
method of moments
trigamma function
variance
title Closed-form estimators for the inverse Nakagami distribution
title_full Closed-form estimators for the inverse Nakagami distribution
title_fullStr Closed-form estimators for the inverse Nakagami distribution
title_full_unstemmed Closed-form estimators for the inverse Nakagami distribution
title_short Closed-form estimators for the inverse Nakagami distribution
title_sort closed form estimators for the inverse nakagami distribution
topic digamma function
method of moments
trigamma function
variance
url http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652025000100401&tlng=en
work_keys_str_mv AT victornawa closedformestimatorsfortheinversenakagamidistribution
AT saraleesnadarajah closedformestimatorsfortheinversenakagamidistribution