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
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Artifact Removal ◽  
Ringing Artifact

The extended algorithm for quasi maximum likelihood parameter estimation

2021 ◽  
Author(s):  
Alfira Mulya Astuti ◽  
Setiawan ◽  
Ismaini Zain ◽  
Jerry D. T. Purnomo
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Quasi Maximum Likelihood

Maximum-Likelihood Event Parameter Estimation from Digital Waveform Capture

2017 ◽  
Author(s):  
Neil C. Momsen ◽  
Garrett Richards ◽  
Matthew A. Kupinski ◽  
Harrison H. Barrett ◽  
Lars R. Furenlid
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood

scholarly journals ESTIMATION OF THE BINARY LOGISTIC REGRESSION MODEL PARAMETER USING BOOTSTRAP RE-SAMPLING

2018 ◽  
Vol 48 (3) ◽  
pp. 199-204 ◽  
Author(s):  
R. LI ◽  
J. ZHOU ◽  
L. WANG
Keyword(s):  
Logistic Regression ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Parametric Bootstrap ◽  
Binary Logistic Regression Model ◽  
Bayesian Bootstrap ◽  
Non Parametric

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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