Quantile Regression Under Asymmetric Laplace Distribution in Capital Asset Pricing Model

Autchariyapanitkul, Kittawit; Chanaim, Somsak; Sriboonchitta, Songsak

doi:10.1007/978-3-319-13449-9_15

Cited by 7 publications

(2 citation statements)

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“…Laplace distribution is a commonly used distribution for modeling lifetime in engineering field. Because of its distribution with peak and thick tail, it is a more suitable distribution to describe the financial data than the normal distribution, so it has more research and application in the field of finance [1][2][3][4][5][6][7]. In recent years, the distribution has also been applied to image analysis, mechanical engineering and other fields [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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

Li¹

2017

IJDSA

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Loss function is one of the most topics in Bayesian analysis. The aim of this paper is to study the estimation of the shape parameter of Laplace distribution using Bayesian technique under a new loss function, which is a compound function of LINEX function. The Bayes estimator of the parameter is derived under the prior distribution of the parameter based on Gamma prior distribution. Furthermore, Monte Carlo statistical simulations illustrate that the Bayes estimators obtained under LINEX-based loss function is affected by the prior parameter and the value of the shape parameter of the LINEX-based loss function. But when the sample size is large, they have less influence on the estimation result.

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