Rank and Signed-Rank Tests for Random Coefficient Regression Model

2016 ◽  
Vol 5 (2) ◽  
pp. 233-247 ◽  
Author(s):  
Mohamed Fihri ◽  
Amal Mellouk ◽  
Abdelhadi Akharif
Keyword(s):  
Regression Model ◽  
Random Coefficient ◽  
Rank Tests ◽  
Signed Rank ◽  
Random Coefficient Regression

scholarly journals An application of latent class random coefficient regression

2004 ◽  
Vol 8 (4) ◽  
pp. 247-260 ◽  
Author(s):  
Lars Erichsen ◽  
Per Bruun Brockhoff
Keyword(s):  
Regression Model ◽  
Latent Class ◽  
Likelihood Estimation ◽  
Random Coefficient ◽  
Unknown Parameters ◽  
Latent Class Regression ◽  
Preference Mapping ◽  
Model Combining ◽  
The Em Algorithm ◽  
Random Coefficient Regression

In this paper we apply a statistical model combining a random coefficient regression model and a latent class regression model. The EM-algorithm is used for maximum likelihood estimation of the unknown parameters in the model and it is pointed out how this leads to a straightforward handling of a number of different variance/covariance restrictions. Finally, the model is used to analyze how consumers' preferences for eight coffee samples relate to sensory characteristics of the coffees. Within this application the analysis corresponds to a model-based version of the so-called external preference mapping.

Application of Random Coefficient Regression Model to Myopia Data: A Case Study

1995 ◽  
Vol 37 (6) ◽  
pp. 657-672 ◽  
Author(s):  
Jyrki Möttonen ◽  
Hannu Oja ◽  
Ulf Krause ◽  
Paula Rantakallio
Keyword(s):  
Regression Model ◽  
Random Coefficient ◽  
Random Coefficient Regression
Sign in / Sign up

Export Citation Format

Share Document