Finite Mixture of Linear Regression Models: An Adaptive Constrained Approach to Maximum Likelihood Estimation

2016 ◽  
pp. 181-186
Author(s):  
Roberto Di Mari ◽  
Roberto Rocci ◽  
Stefano Antonio Gattone
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Maximum Likelihood Estimation ◽  
Regression Models ◽  
Likelihood Estimation ◽  
Finite Mixture ◽  
Linear Regression Models

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.

Sign in / Sign up

Export Citation Format

Share Document