Improved maximum-likelihood estimation of the shape parameter in the Nakagami distribution

Schwartz, Jacob T.; Godwin, Ryan T.; Giles, David E. A.

doi:10.1080/00949655.2011.615316

Cited by 41 publications

(19 citation statements)

References 19 publications

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“…It is essential that the proposed method be compared with other prominent conventional methods. The moment-based estimation [2], enhanced moment-based method [3], ML-based estimation [5], and GMM procedure [4] are known as the conventional methods. The mentioned methods are compared with the two proposed copula-based estimators.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Analytic Nakagami fading parameter estimation in dependent noise channel using copula

Gholizadeh

Amindavar

Ritcey

2013

EURASIP J. Adv. Signal Process.

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In this paper, the probability density function (PDF) estimation is introduced in the framework of estimating the Nakagami fading parameter. This approach provides an analytic procedure for finding the fading parameter. Using the copula theory, an accurate PDF estimate is obtained even when the desired signal is corrupted in a noisy environment. In the real world, the noise samples could be highly dependent on the main signal. Copula-based models are a general set of statistical models defined for any multivariate random variable. Thus, they depict the statistical behavior of a received signal including two dependent terms, representative of the desired signal and noise. Previous works in the Nakagami parameter determination have mainly examined estimation based on either a noiseless sample model or an independent trivial noisy one. In this paper, we consider a more comprehensive situation about the noise destruction and our investigation is done in low signal-to-noise ratios. The parametric bootstrap method approves the accuracy of the analytically estimated PDF, and simulation results show that the new estimator has superior performance over conventional estimators.

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Section: Simulation and Resultsmentioning

confidence: 99%

Analytic Nakagami fading parameter estimation in dependent noise channel using copula

Gholizadeh

Amindavar

Ritcey

2013

EURASIP J. Adv. Signal Process.

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“…For example, communications engineering, hydrology, medical imaging studies, multimedia data traffic over networks, modelling of high-frequency seismogram envelopes etc. A list of references using these applications can be found in Schwartz et al (2013). Abdi and Kaveh (2000) compared three different estimators for the Nakagami-m parameter using Monte Carlo simulation.…”

Section: Introductionmentioning

confidence: 99%

“…Cheng and Beaulieu (2001) described maximum likelihood based estimation of the Nakagami-m parameter. Recently, Schwartz et al (2013) discussed improved maximum likelihood estimation of the shape parameter in the Nakagami distribution. Also, they gave some distributional properties of the Nakagami distribution.…”

Section: Introductionmentioning

confidence: 99%

Nakagami distribution as a reliability model under progressive censoring

Kumar

Garg

Krishna

2016

Int J Syst Assur Eng Manag

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The Nakagami distribution is widely used in communication engineering. In this article we consider this distribution as a useful life time model in life testing experiments and reliability theory. Some of its distributional properties and reliability characteristics are discussed. In order to reduce cost and time of life testing experiments progressive type II censoring is used. Maximum likelihood (ML) and least square estimators of the unknown parameters and reliability characteristics are derived with progressively type II censored sample from this distribution. Interval estimation and coverage probability based on ML estimates are obtained. Monte Carlo simulation study is performed to compare various estimates developed. Findings are illustrated by three examples, two based on simulated data sets and one consisting of a real data set.

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“…Se comparada com outras distribuições, a Nakagami é considerada genérica, de grande flexibilidade e simplicidade matemática. O número de aplicações na área climatológica é ainda pequeno e podese citar os trabalhos recentes dos autores Schwartz et al (2013), Singh e Sarkar (2013) e Mazucheli e Emanuelli (2015), sendo que apenas neste último a distribuição foi ajustada a dados referentes ao volume de precipitação.…”

Section: Introductionunclassified

Aplicação Do Método PBC Para Avaliação Da Complexidade Das Distribuições Gama E Nakagami Na Análise De Dados De Precipitação

Fernandes¹,

Mazucheli²,

Emanuelli³

2017

Enci Bio

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RESUMOEm geral, a seleção da distribuição de probabilidade com o melhor ajuste não considera a complexidade das distribuições rivais. O método PBC (Parametric Bootstrap Cross-Fitting) mede o viés causado pelo mimetismo das distribuições candidatas, levando em consideração a avaliação da complexidade das mesmas. Considerando a importância de explicar o comportamento dos volumes de precipitações mensais e da possibilidade do uso do método PBC para comparação do ajuste de duas distribuições concorrentes, objetivou-se neste trabalho realizar a discriminação e a comparação do mimetismo das distribuições Gama e Nakagami, aplicando o método PBC aos dados de precipitações mensais da estação meteorológica convencional de Maringá -PR, observados entre 1964 e 2016. Embora Nakagami não seja uma distribuição muito utilizada em estudos envolvendo variáveis de natureza climatológica, os resultados referentes a aplicação do método PBC indicam que para a maior parte das séries históricas mensais, a distribuição mais apropriada para a descrição do comportamento da precipitação mensal é a Nakagami quando comparada a Gama. Ainda, foi verificado que a Gama é funcionalmente mais complexa em relação a Nakagami, apresentando maior viés causado pelo mimetismo em todas as séries de precipitação mensal total. PALAVRAS-CHAVE: Climatologia; Distribuição de Probabilidade; Método CrossFitting; APPLICATION OF THE PBC METHOD FOR THE EVALUATION OF COMPLEXITY OF THE GAMA AND NAKAGAMI DISTRIBUTIONS IN THE ANALYSIS OF PRECIPITATION DATAABSTRACT In general, the selection of the probability distribution with the best fit does not take into account the distributions' complexity. The PBC (Parametric Bootstrap CrossFitting) method quantifies the bias due to the mimicry of the candidate distributions, taking into account the evaluation of the models' complexity. Considering the importance of explaining the behavior of the monthly rainfall volumes and the possibility of using the PBC method to compare the fit of two competing distributions, the objective of this work is to discriminate and compare the mimicry of the Gamma and Nakagami distributions, by applying the PBC method to the monthly precipitation

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Improved maximum-likelihood estimation of the shape parameter in the Nakagami distribution

Cited by 41 publications

References 19 publications

Analytic Nakagami fading parameter estimation in dependent noise channel using copula

Analytic Nakagami fading parameter estimation in dependent noise channel using copula

Nakagami distribution as a reliability model under progressive censoring

Aplicação Do Método PBC Para Avaliação Da Complexidade Das Distribuições Gama E Nakagami Na Análise De Dados De Precipitação

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