In this article, a new approach for model specification is proposed. The method allows to choose the correct order of a mixture model by testing, if a particular mixture component is significant. The hypotheses are set in a new way, in order to avoid identification problems, which are typical for mixture models. If some of the parameters are known, the distribution of the LR statistic is Chi2, with the degrees of freedom depending on the number of components and the number of parameters in each component. The advantage of the new approach is its simplicity and computational feasibility
The consistent estimation of mixture complexity is of fundamental importance in many applications of...
The purpose of this thesis is to give insight into major problems arising in the theory of mixture d...
Finite mixtures of regression models with random effects are a very flexible statistical tool to mod...
In this article, a new approach for model specification is proposed. The method allows to choose the...
Over the last decades, linear models have been studied by the scientific community as an important t...
Despite the popularity of mixture regression models, the decision of how many components to retain r...
TEZ5510Tez (Yüksek Lisans) -- Çukurova Üniversitesi, Adana, 2005.Kaynakça (s. 100-106) var.xi, 107 s...
October 2012This paper considers likelihood-based testing of the null hypothesis of m0 components ag...
<p>Testing the number of components in finite normal mixture models is a long-standing challenge bec...
Mixture regression model has been proven to be a useful tool in the study of heterogeneous populatio...
This paper considers likelihood-based testing of the null hypothesis of m0 components against the al...
Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogenei...
This article evaluates a new Bayesian approach to determining the number of components in a finite m...
This paper presents a new approach which can be used to determine the optimum number of components i...
The mixture approach to clustering requires the user to specify both the number of components to be ...
The consistent estimation of mixture complexity is of fundamental importance in many applications of...
The purpose of this thesis is to give insight into major problems arising in the theory of mixture d...
Finite mixtures of regression models with random effects are a very flexible statistical tool to mod...
In this article, a new approach for model specification is proposed. The method allows to choose the...
Over the last decades, linear models have been studied by the scientific community as an important t...
Despite the popularity of mixture regression models, the decision of how many components to retain r...
TEZ5510Tez (Yüksek Lisans) -- Çukurova Üniversitesi, Adana, 2005.Kaynakça (s. 100-106) var.xi, 107 s...
October 2012This paper considers likelihood-based testing of the null hypothesis of m0 components ag...
<p>Testing the number of components in finite normal mixture models is a long-standing challenge bec...
Mixture regression model has been proven to be a useful tool in the study of heterogeneous populatio...
This paper considers likelihood-based testing of the null hypothesis of m0 components against the al...
Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogenei...
This article evaluates a new Bayesian approach to determining the number of components in a finite m...
This paper presents a new approach which can be used to determine the optimum number of components i...
The mixture approach to clustering requires the user to specify both the number of components to be ...
The consistent estimation of mixture complexity is of fundamental importance in many applications of...
The purpose of this thesis is to give insight into major problems arising in the theory of mixture d...
Finite mixtures of regression models with random effects are a very flexible statistical tool to mod...