Posterior Properties of the Nakagami-m Distribution Using Noninformative Priors and Applications in Reliability

Ramos, Pedro Luiz; Louzada, Francisco; Ramos, Eduardo

doi:10.1109/tr.2017.2778139

Cited by 18 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding the parameter estimators of the proposed model, many different inferential procedures could be considered in this study (see, for instance, Bakouch et al [2] or Ramos et al [11]), however due to the large number of observations the different estimators will return similar results. Therefore, we considered the MLEs as they are implemented in R packages.…”

Section: Modeling Results Under Parametric Modelsmentioning

confidence: 99%

Medical Care in Emergency Units with Risk Classification: Time to Attendance at Hospital based on Parametric Models

Ramos

Nascimento

Fernandes

et al. 2019

Tend. Mat. Apl. Comput.

Self Cite

View full text Add to dashboard Cite

The present work intends to study the effectiveness of applying Manchester Triage System to improve patient flow in a Brazilian hospital, which allow a more welcoming and decisive service. Thus, time to event techniques are applied based on parametric regression models with the objective of investigating indicators for the emergency/urgency sector and thus, contributing to better operational efficiency. The results show that different explanatory variables such as classification, age, period, among others, influence in the time of attendance. At the end, we provide a simple model that can be used to predict such time under different explanatory variable for a particular Brazilian hospital.

show abstract

Section: Modeling Results Under Parametric Modelsmentioning

confidence: 99%

Medical Care in Emergency Units with Risk Classification: Time to Attendance at Hospital based on Parametric Models

Ramos

Nascimento

Fernandes

et al. 2019

Tend. Mat. Apl. Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…These assumed prior distributions have been used widely by several authors including [22][23][24][25][26][27][28][29]. This study also considers three loss functions including square error, quadratic and precautionary loss functions which have also been used previously by some researchers such as [30][31][32][33][34][35][36][37][38][39][40] etc. The stated prior distributions and loss functions are defined as follows:…”

Section: Bayesian Estimationmentioning

confidence: 99%

Bayesian Analysis of Weibull-Lindley Distribution Using Different Loss Functions

Eraikhuemen

Bamigbala

Magaji

et al. 2020

AJARR

View full text Add to dashboard Cite

In the present paper, a three-parameter Weibull-Lindley distribution is considered for Bayesian analysis. The estimation of a shape parameter of Weibull-Lindley distribution is obtained with the help of both the classical and Bayesian methods. Bayesian estimators are obtained by using Jeffrey's prior, uniform prior and Gamma prior under square error loss function, quadratic loss function and Precautionary loss function. Estimation by the method of Maximum likelihood is also discussed. These methods are compared by using mean square error through simulation study with varying parameter values and sample sizes. AJARR, 8(4): 28-41, 2020; Article no.AJARR.54833 29 Original Research Article

show abstract

“…These assumed prior distributions have been used widely by several authors including [19][20][21][22][23][24][25][26][27]. This study also considers three loss functions including square error, quadratic and precautionary loss functions which have also been used previously by some researchers such as [28][29][30][31][32][33][34][35][36][37][38] etc. The study also considered deriving the estimators of the shape parameter in closed-form using the Bayesian approach because of the usefulness of Closed-form estimators as recently demonstrated by [39] and [40].…”

Section: Bayesian Estimationmentioning

confidence: 99%

Bayesian and Maximum Likelihood Estimation of the Shape Parameter of Exponential Inverse Exponential Distribution: A Comparative Approach

Eraikhuemen

Mohammed

Sule

2020

AJPAS

View full text Add to dashboard Cite

This paper aims at making Bayesian analysis on the shape parameter of the exponential inverse exponential distribution using informative and non-informative priors. Bayesian estimation was carried out through a Monte Carlo study under 10,000 replications. To assess the effects of the assumed prior distributions and loss function on the Bayesian estimators, the mean square error has been used as a criterion. Overall, simulation results indicate that Bayesian estimation under QLF outperforms the maximum likelihood estimation and Bayesian estimation under alternative loss functions irrespective of the nature of the prior and the sample size. Also, for large sample sizes, all methods perform equally well.

show abstract

Posterior Properties of the Nakagami-m Distribution Using Noninformative Priors and Applications in Reliability

Cited by 18 publications

References 27 publications

Medical Care in Emergency Units with Risk Classification: Time to Attendance at Hospital based on Parametric Models

Medical Care in Emergency Units with Risk Classification: Time to Attendance at Hospital based on Parametric Models

Bayesian Analysis of Weibull-Lindley Distribution Using Different Loss Functions

Bayesian and Maximum Likelihood Estimation of the Shape Parameter of Exponential Inverse Exponential Distribution: A Comparative Approach

Contact Info

Product

Resources

About