This paper is concerned with the problem of finding the minimax estimator of the parameter θ of the Rayleigh distribution for quadratic loss function by applying the theorem of Lehmann (1950).
How to Cite:
Dey, S., 2008. Minimax Estimation of the Parameter of the Rayleigh Distribution under Quadratic Loss Function. Data Science Journal, 7, pp.23–30. DOI: http://doi.org/10.2481/dsj.7.23
Dey, S., 2008. Minimax Estimation of the Parameter of the Rayleigh Distribution under Quadratic Loss Function. Data Science Journal, 7, pp.23–30. DOI: http://doi.org/10.2481/dsj.7.23
Dey S. Minimax Estimation of the Parameter of the Rayleigh Distribution under Quadratic Loss Function. Data Science Journal. 2008;7:23–30. DOI: http://doi.org/10.2481/dsj.7.23
Dey, S. (2008). Minimax Estimation of the Parameter of the Rayleigh Distribution under Quadratic Loss Function. Data Science Journal, 7, 23–30. DOI: http://doi.org/10.2481/dsj.7.23
Dey S, ‘Minimax Estimation of the Parameter of the Rayleigh Distribution Under Quadratic Loss Function’ (2008) 7 Data Science Journal 23 DOI: http://doi.org/10.2481/dsj.7.23
Dey, Sanku. 2008. “Minimax Estimation of the Parameter of the Rayleigh Distribution Under Quadratic Loss Function”. Data Science Journal 7: 23–30. DOI: http://doi.org/10.2481/dsj.7.23
Dey, Sanku. “Minimax Estimation of the Parameter of the Rayleigh Distribution Under Quadratic Loss Function”. Data Science Journal 7 (2008): 23–30. DOI: http://doi.org/10.2481/dsj.7.23
Dey, S.. “Minimax Estimation of the Parameter of the Rayleigh Distribution Under Quadratic Loss Function”. Data Science Journal, vol. 7, 2008, pp. 23–30. DOI: http://doi.org/10.2481/dsj.7.23
