An Importance Sampling EM Algorithm for Latent Regression Models

2007 ◽  
Vol 32 (3) ◽  
pp. 233-251 ◽  
Author(s):  
Matthias von Davier ◽  
Sandip Sinharay
Keyword(s):  
Em Algorithm ◽  
Importance Sampling ◽  
Regression Models ◽  
Latent Regression

Testing the Fit of Latent Regression Models

2004 ◽  
Vol 33 (6) ◽  
pp. 1341-1356 ◽  
Author(s):  
Karl Bang Christensen ◽  
Svend Kreiner
Keyword(s):  
Regression Models ◽  
Latent 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.

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