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.

scholarly journals Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2076
Author(s):  
Seohee Park ◽  
Seongeun Kim ◽  
Ji Hoon Ryoo
Keyword(s):  
Latent Class ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Component Analysis ◽  
Small Sample ◽  
Multivariate Normality ◽  
Generalized Structured Component Analysis ◽  
Latent Class Regression ◽  
Normality Assumption ◽  
Fuzzy Clustering Method

Latent class analysis (LCA) has been applied in many research areas to disentangle the heterogeneity of a population. Despite its popularity, its estimation method is limited to maximum likelihood estimation (MLE), which requires large samples to satisfy both the multivariate normality assumption and local independence assumption. Although many suggestions regarding adequate sample sizes were proposed, researchers continue to apply LCA with relatively smaller samples. When covariates are involved, the estimation issue is encountered more. In this study, we suggest a different estimating approach for LCA with covariates, also known as latent class regression (LCR), using a fuzzy clustering method and generalized structured component analysis (GSCA). This new approach is free from the distributional assumption and stable in estimating parameters. Parallel to the three-step approach used in the MLE-based LCA, we extend an algorithm of fuzzy clusterwise GSCA into LCR. This proposed algorithm has been demonstrated with an empirical data with both categorical and continuous covariates. Because the proposed algorithm can be used for a relatively small sample in LCR without requiring a multivariate normality assumption, the new algorithm is more applicable to social, behavioral, and health sciences.

scholarly journals Cigarette Smoking and Multiple Health Risk Behaviors: A Latent Class Regression Model to Identify a Profile of Young Adolescents

Risk Analysis ◽  
2019 ◽  
Vol 39 (8) ◽  
pp. 1771-1782 ◽  
Author(s):  
Lorena Charrier ◽  
Paola Berchialla ◽  
Paola Dalmasso ◽  
Alberto Borraccino ◽  
Patrizia Lemma ◽  
...  
Keyword(s):  
Cigarette Smoking ◽  
Health Risk ◽  
Regression Model ◽  
Risk Behaviors ◽  
Latent Class ◽  
Health Risk Behaviors ◽  
Young Adolescents ◽  
Latent Class Regression ◽  
Multiple Health

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
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