Bayesian estimation and posterior risk under the generalized weighted squared error loss function and its applications

被引:0
作者
Han, Ming [1 ]
机构
[1] Ningbo Univ Technol, Sch Stat & Data Sci, Ningbo 315211, Zhejiang, Peoples R China
关键词
Bayesian estimation; Posterior risk; Generalized weighted squared error loss function; Weighted squared-negative exponential error loss function; LINEX loss function; RAYLEIGH DISTRIBUTION; STATISTICAL-INFERENCE; PREDICTION; PARAMETER; MODEL;
D O I
10.1007/s00362-024-01643-0
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
This paper proposed a new family of loss function, as a generalization of weighted squared error loss function, aiming to construct more new loss functions. We proposed the definition of the generalized weighted squared error loss function and derived the Bayesian estimation and its posterior risk under the generalized weighted squared error loss function based on the definition. Moreover, some members of the family of the generalized weighted squared error loss function are discussed. The results show that the proposed generalized weighted squared error loss function contains some existing loss functions in special cases and can obviously be employed to generate more new ones. The expressions of Bayesian estimations and their posterior risks of Rayleigh distribution parameter under the squared error loss function, weighted squared-negative exponential error loss function and LINEX loss function are derived respectively. For ease of explanation, Monte Carlo simulation example and application example are provided, and the results are compared based on posterior risk.
引用
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页数:26
相关论文
共 28 条
[1]  
Al-Bayyati H N, 2002, Comparing methods of estimating Weibull failure models using simulation
[2]  
AL-Shreefi E F, 2016, C IR STAT ASS ISA
[3]   A study of the effect of the loss function on Bayes Estimate, posterior risk and hazard function for Lindley distribution [J].
Ali, Sajid ;
Aslam, Muhammad ;
Kazmi, Syed Mohsin Ali .
APPLIED MATHEMATICAL MODELLING, 2013, 37 (08) :6068-6078
[4]  
[Anonymous], 1880, LOND EDINB DUBLIN PH, DOI 10.1080/14786448008626893
[5]   APPROXIMATE MLE OF THE SCALE PARAMETER OF THE RAYLEIGH DISTRIBUTION WITH CENSORING [J].
BALAKRISHNAN, N .
IEEE TRANSACTIONS ON RELIABILITY, 1989, 38 (03) :355-357
[6]  
Berger J.O., 1985, Statistical Decision Theory and Bayesian Analysis, P118, DOI [DOI 10.1007/978-1-4757-4286-2, https://doi.org/10.1007/978-1-4757-4286-2]
[7]  
Dey S., 2009, MALAYS J MATH SCI, V3, P249
[8]   Statistical Inference for the Rayleigh distribution under progressively Type-II censoring with binomial removal [J].
Dey, Sanku ;
Dey, Tanujit .
APPLIED MATHEMATICAL MODELLING, 2014, 38 (03) :974-982
[9]   Bayesian estimation and prediction intervals for a Rayleigh distribution under a conjugate prior [J].
Dey, Sanku ;
Dey, Tanujit .
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2012, 82 (11) :1651-1660
[10]   Bayesian estimation of the parameter of maxwell distribution under different loss functions [J].
Dey S. ;
Maiti S.S. .
Journal of Statistical Theory and Practice, 2010, 4 (2) :279-287