Maximum-likelihood parameter estimation for image ringing-artifact removal

2001 ◽  
Vol 11 (8) ◽  
pp. 963-973 ◽  
Author(s):  
Seungjoon Yang ◽  
Yu-Hen Hu ◽  
T.Q. Nguyen ◽  
D.L. Tull
2021 ◽  
Author(s):  
Alfira Mulya Astuti ◽  
Setiawan ◽  
Ismaini Zain ◽  
Jerry D. T. Purnomo

Author(s):  
Neil C. Momsen ◽  
Garrett Richards ◽  
Matthew A. Kupinski ◽  
Harrison H. Barrett ◽  
Lars R. Furenlid

2018 ◽  
Vol 48 (3) ◽  
pp. 199-204 ◽  
Author(s):  
R. LI ◽  
J. ZHOU ◽  
L. WANG

In this paper, the non-parametric bootstrap and non-parametric Bayesian bootstrap methods are applied for parameter estimation in the binary logistic regression model. A real data study and a simulation study are conducted to compare the Nonparametric bootstrap, Non-parametric Bayesian bootstrap and the maximum likelihood methods. Study results shows that three methods are all effective ways for parameter estimation in the binary logistic regression model. In small sample case, the non-parametric Bayesian bootstrap method performs relatively better than the non-parametric bootstrap and the maximum likelihood method for parameter estimation in the binary logistic regression model.


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