Bayes Estimation of Parameter of Laplace Distribution Under a New LINEX-Based Loss Function

Li, Lanping

doi:10.11648/j.ijdsa.20170306.14

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…N could be negative for smaller values of n , hence proposed a biassed estimator for μ 2 as

and looked at its huge sample features [ 28 ]. To generate an estimator with a certain mean square error as S but a reduced bias than S for huge sample sizes n ,

…”

Section: Proposed Methodologymentioning

confidence: 99%

Bayesian Estimation of Different Scale Parameters Using a LINEX Loss Function

Mohammed

Alaziz

Sumati

et al. 2022

Computational Intelligence and Neuroscience

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The LINEX loss function, which climbs exponentially with one-half of zero and virtually linearly on either side of zero, is employed to analyze parameter analysis and prediction problems. It can be used to solve both underestimation and overestimation issues. This paper explained the Bayesian estimation of mean, Gamma distribution, and Poisson process. First, an improved estimator for μ 2 is provided (which employs a variation coefficient). Under the LINEX loss function, a better estimator for the square root of the median is also derived, and an enhanced estimation for the average mean in such a negatively exponential function. Second, giving a gamma distribution as a prior and a likelihood function as posterior yields a gamma distribution. The LINEX method can be used to estimate an estimator λ B L ^ using posterior distribution. After obtaining λ B L ^ , the hazard function h B L ^ and D B L ^ the function of survival estimators are used. Third, the challenge of sequentially predicting the intensity variable of a uniform Poisson process with a linear exponentially (LINEX) loss function and a constant cost of production time is investigated using a Bayesian model. The APO rule is offered as an approximation pointwise optimal rule. LINEX is the loss function used. A variety of prior distributions have already been studied, and Bayesian estimation methods have been evaluated against squared error loss function estimation methods. Finally, compare the results of Maximum Likelihood Estimation (MLE) and LINEX estimation to determine which technique is appropriate for such information by identifying the lowest Mean Square Error (MSE). The displaced estimation method under the LINEX loss function was also examined in this research, and an improved estimation was proposed.

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“…N could be negative for smaller values of n , hence proposed a biassed estimator for μ 2 as

and looked at its huge sample features [ 28 ]. To generate an estimator with a certain mean square error as S but a reduced bias than S for huge sample sizes n ,

…”

Section: Proposed Methodologymentioning

confidence: 99%

Bayesian Estimation of Different Scale Parameters Using a LINEX Loss Function

Mohammed

Alaziz

Sumati

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The Laplace distribution with location parameter zero and scale parameter one is called the classical Laplace distribution and its p.d.f is given by Several modifications of the Laplace distribution are currently available in the literature. Some recent studies in this respect were made by Cordeiro and Lemonte (2011), Jose and Thomas (2014), Liu and Kozubowski (2015), Mahmoudvand et al (2015), Kozubowski et al (2016), Nassar (2016) and Li (2017). Laplace distribution has wide range of applications in real life to model and analyze data sets in engineering, financial, industrial, environmental and biological fields.…”

Section: Introductionmentioning

confidence: 99%

An alternative Laplace distribution

Kumar

Jose

2020

JSR

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In this paper, we propose an alternative version to the Laplace distribution which we named as “alternative Laplace distribution (ALD)” and discuss some of its important properties. A location-scale extension of the ALD is considered and the maximum likelihood estimation procedures for estimating its parameters is described. Further, the distribution is ﬁtted to certain real life data sets for illustrating the utility of the model. A simulation study is carried out to examine the performance of likelihood estimators of the parameters of the distribution.

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Statistical Measures

Domański¹

2019

Studies in Systems, Decision and Control

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Bayes Estimation of Parameter of Laplace Distribution Under a New LINEX-Based Loss Function

Cited by 5 publications

References 24 publications

Bayesian Estimation of Different Scale Parameters Using a LINEX Loss Function

Bayesian Estimation of Different Scale Parameters Using a LINEX Loss Function

An alternative Laplace distribution

Statistical Measures

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