A Mathai-type Multivariate Nakagami-m Distribution: Density, Closed-form Estimators, and Asymptotic Efficiency
Abstract
In this study, we propose a novel multivariate Nakagami-m distribution whose joint probability density function admits a fully closed-form expression. The proposed density is derived using a Mathai-type construction based on the partial sums of independent gamma random variables. For this distribution, the maximum likelihood estimator is not available in the closed form. To address this limitation, an asymptotically efficient closed-form estimator is developed by applying a one-step refinement to a √n-consistent initial estimator. Monte Carlo simulations demonstrate that the proposed estimator achieves a performance nearly identical to that of the numerically computed maximum likelihood estimator, while consistently outperforming the closed-form initial estimators. A real-data application further illustrates its practical utility.
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BibTeX
@misc{kim2026mathaitype,
title = {{A Mathai-type Multivariate Nakagami-$m$ Distribution: Density, Closed-form Estimators, and Asymptotic Efficiency}},
author = {Hyeonwoo Kim and Hyoung-Moon Kim},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.14483},
eprint = {2609.14483},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.14483},
}
RIS
TY - UNPB
TI - A Mathai-type Multivariate Nakagami-m Distribution: Density, Closed-form Estimators, and Asymptotic Efficiency
AU - Hyeonwoo Kim
AU - Hyoung-Moon Kim
PY - 2026
DA - 2026/09/13
PB - arXiv
DO - 10.48550/arxiv.2609.14483
UR - https://arxiv.org/abs/2609.14483
AB - In this study, we propose a novel multivariate Nakagami-m distribution whose joint probability density function admits a fully closed-form expression. The proposed density is derived using a Mathai-type construction based on the partial sums of independent gamma random variables. For this distribution, the maximum likelihood estimator is not available in the closed form. To address this limitation, an asymptotically efficient closed-form estimator is developed by applying a one-step refinement to a √n-consistent initial estimator. Monte Carlo simulations demonstrate that the proposed estimator achieves a performance nearly identical to that of the numerically computed maximum likelihood estimator, while consistently outperforming the closed-form initial estimators. A real-data application further illustrates its practical utility.
ER -