scholarly journals On The Convergence Of The Maximum Likelihood Estimator Method Of Tomographic Image Reconstruction

1987 ◽  
Author(s):  
Jorge Llacer ◽  
Eugene Veklerov ◽  
Edward J. Hoffman ◽  
Samuel J. Dwyer III ◽  
Roger H. Schneider
Keyword(s):  
Maximum Likelihood ◽  
Image Reconstruction ◽  
Maximum Likelihood Estimator ◽  
Tomographic Image ◽  
Likelihood Estimator ◽  
Tomographic Image Reconstruction

scholarly journals Maximum-likelihood dual-energy tomographic image reconstruction

2002 ◽  
Author(s):  
Jeffrey A. Fessler ◽  
Idris A. Elbakri ◽  
Predrag Sukovic ◽  
Neal H. Clinthorne
Keyword(s):  
Maximum Likelihood ◽  
Image Reconstruction ◽  
Dual Energy ◽  
Tomographic Image ◽  
Tomographic Image Reconstruction

The Comparison Between the Bayes Estimator and the Maximum Likelihood Estimator of the Reliability Function for Negative Exponential Distribution

2018 ◽  
pp. 439
Author(s):  
Hazim Mansour Gorgees ◽  
Bushra Abdualrasool Ali ◽  
Raghad Ibrahim Kathum
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Exponential Distribution ◽  
Reliability Function ◽  
Bayes Estimator ◽  
Likelihood Estimator ◽  
Monte Carlo Simulation Technique ◽  
Negative Exponential Distribution ◽  
Negative Exponential ◽  
Better Than

     In this paper, the maximum likelihood estimator and the Bayes estimator of the reliability function for negative exponential distribution has been derived, then a Monte –Carlo simulation technique was employed to compare the performance of such estimators. The integral mean square error (IMSE) was used as a criterion for this comparison. The simulation results displayed that the Bayes estimator performed better than the maximum likelihood estimator for different samples sizes.

scholarly journals Transforming variables to central normality

2021 ◽  
Author(s):  
Jakob Raymaekers ◽  
Peter J. Rousseeuw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simulation Study ◽  
Real Data ◽  
Data Sets ◽  
Transformation Parameter ◽  
Likelihood Estimator ◽  
Extensive Simulation ◽  
Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

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