Recent Advances in Linear Models and Related Areas 2008
DOI: 10.1007/978-3-7908-2064-5_11
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Finite Mixtures of Generalized Linear Regression Models

Abstract: Summary. Generalized linear models have become a standard technique in the statistical modelling toolbox for investigating relationships between variables. The assumption of homogeneity of regression coefficients over all observations can be relaxed by incorporating generalized linear models into the finite mixture framework.The model class consisting of finite mixtures of generalized linear models is presented. Model identification is discussed given that difficulties might be encountered due to trivial and g… Show more

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Cited by 57 publications
(53 citation statements)
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“…We further established identifiability results for the proposed two models under mild conditions. In particular, our result verifies that the semiparametric and nonparametric mixture models proposed by Grün and Leisch (2008a), Young and Hunter (2010), and Cao and Yao (2012) are identifiable under the conditions given in Theorems 3.1 and 3.2.…”
Section: Discussionsupporting
confidence: 50%
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“…We further established identifiability results for the proposed two models under mild conditions. In particular, our result verifies that the semiparametric and nonparametric mixture models proposed by Grün and Leisch (2008a), Young and Hunter (2010), and Cao and Yao (2012) are identifiable under the conditions given in Theorems 3.1 and 3.2.…”
Section: Discussionsupporting
confidence: 50%
“…Identifiability results for finite mixtures of ordinary linear models and finite mixtures of GLMs are available, see Hennig (2000) and Grün and Leisch (2008a); but these results are not applicable to models (2.2) and (2.3) as some of parameters are nonparametric functions of covariates.…”
Section: Identifiabilitymentioning
confidence: 99%
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“…General conditions for identifiability of mixtures of linear models can be found in Hennig (2000); based on such results, Grün and Leisch (2008a) provided results about identifiability for model (8). Follmann and Lambert (1991) and Wang (1994) established identifiability results for mixtures of logistic regression models (the latter paper considered the case with concomitant variables).…”
Section: Identifiabilitymentioning
confidence: 99%