1
Department of Statistics, Payam Noor University, Tehran, Iran
2
Department of Statistics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran
Abstract
When approximate and initial information about the unknown parameter of a distribution is available, the contraction estimation method can be used to estimate it. In this paper, first, the E-Bayes estimate of the inverse Rayleigh distribution parameter under the general entropy loss function is obtained and then, using the estimated value of the inverse Rayleigh distribution parameter, its contraction estimate is presented. Also, using Monte Carlo simulation and a real data set, the proposed contraction estimate is compared with the unbiased least-variance and E-Bayes estimates based on the relative efficiency criterion.
Yaghoubzadeh Shahrestani, S. & Zarei, R. (2020). E-Bayes approach in the contraction estimation of the inverse Rayleigh distribution parameter under the general entropy loss function. Andishe_ye Amari, 25(1), 111-121.
MLA
Yaghoubzadeh Shahrestani, S., & Zarei, R. "E-Bayes approach in the contraction estimation of the inverse Rayleigh distribution parameter under the general entropy loss function", Andishe_ye Amari, 25, 1, 2020, 111-121.
HARVARD
Yaghoubzadeh Shahrestani S., Zarei R. (2020). 'E-Bayes approach in the contraction estimation of the inverse Rayleigh distribution parameter under the general entropy loss function', Andishe_ye Amari, 25(1), pp. 111-121.
CHICAGO
S. Yaghoubzadeh Shahrestani & R. Zarei, "E-Bayes approach in the contraction estimation of the inverse Rayleigh distribution parameter under the general entropy loss function," Andishe_ye Amari, 25 1 (2020): 111-121,
VANCOUVER
Yaghoubzadeh Shahrestani S., Zarei R. E-Bayes approach in the contraction estimation of the inverse Rayleigh distribution parameter under the general entropy loss function. Andishe_ye Amari. 2020;25(1):111-121 (In Persian).
