scholarly journals A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models

Econometrica ◽  
1974 ◽  
Vol 42 (3) ◽  
pp. 579 ◽  
Author(s):  
W. Oberhofer ◽  
J. Kmenta
Keyword(s):  
Maximum Likelihood ◽  
Regression Models ◽  
General Procedure ◽  
Maximum Likelihood Estimates ◽  
Generalized Regression

scholarly journals Finite mixture of compositional regression with gaussian errors

2018 ◽  
Vol 41 (1) ◽  
pp. 75-86
Author(s):  
Taciana Shimizu ◽  
Francisco Louzada ◽  
Adriano Suzuki
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Regression Model ◽  
Simulation Study ◽  
Regression Models ◽  
Finite Mixture ◽  
Maximum Likelihood Estimates ◽  
The Real ◽  
Compositional Structure ◽  
Volleyball Players

In this paper, we consider to evaluate the efficiency of volleyball players according to the performance of attack, block and serve, but considering the compositional structure of the data related to the fundaments. The finite mixture of regression models better fitted the data in comparison with the usual regression model. The maximum likelihood estimates are obtained via an EM algorithm. A simulation study revels that the estimates are closer to the real values, the estimators are asymptotically unbiased for the parameters. A real Brazilian volleyball dataset related to the efficiency of the players is considered for the analysis.

Catch-–effort maximum likelihood estimation of important population parameters

1997 ◽  
Vol 54 (4) ◽  
pp. 890-897 ◽  
Author(s):  
W R Gould ◽  
K H Pollock
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Regression Models ◽  
Model Development ◽  
Likelihood Estimation ◽  
Maximum Likelihood Estimates ◽  
Linear Regression Models ◽  
Estimation Of Parameters ◽  
Regression Methods ◽  
Regression Techniques

The relative ease with which linear regression models are understood explains the popularity of such techniques in estimating population size with catch-effort data. However, the development and use of the regression models require assumptions and approximations that may not accurately reflect reality. We present the model development necessary for maximum likelihood estimation of parameters from catch-effort data using the program SURVIV, the primary intent being to present biologists with a vehicle for producing maximum likelihood estimates in lieu of using the traditional regression techniques. The differences between the regression approaches and maximum likelihood estimation will be illustrated with an example of commercial fishery catch-effort data and through simulation. Our results indicate that maximum likelihood estimation consistently provides less biased and more precise estimates than the regression methods and allows for greater model flexibility necessary in many circumstances. We recommend the use of maximum likelihood estimation in future catch-effort studies.

scholarly journals New Regression Models Based on the Unit-Sinh-Normal Distribution: Properties, Inference, and Applications

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1231
Author(s):  
Guillermo Martínez-Flórez ◽  
Roger Tovar-Falón
Keyword(s):  
Maximum Likelihood ◽  
Regression Models ◽  
Maximum Likelihood Method ◽  
Real Data ◽  
Random Variable ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Data Sets ◽  
Modeling Data ◽  
Positive Support

In this paper, two new distributions were introduced to model unimodal and/or bimodal data. The first distribution, which was obtained by applying a simple transformation to a unit-Birnbaum–Saunders random variable, is useful for modeling data with positive support, while the second is appropriate for fitting data on the (0,1) interval. Extensions to regression models were also studied in this work, and statistical inference was performed from a classical perspective by using the maximum likelihood method. A small simulation study is presented to evaluate the benefits of the maximum likelihood estimates of the parameters. Finally, two applications to real data sets are reported to illustrate the developed methodology.

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