Identifiability problems can be encountered when fitting finite mixture models and their presence should be investigated by model diagnostics. In this paper we propose diagnostic tools to check for identifiability problems based on the fact that they induce multiple (global) modes in the distribution of the parameterizations of the maximum likelihood models depending on the data generating process. The parametric bootstrap is used to approximate this distribution. In order to investigate the presence of multiple (global) modes the congruence between the results of information-based methods based on asymptotic theory and those derived using the models fitted to the bootstrap samples with initalization in the solution as well as random initia...
AbstractStatistical analyses commonly make use of models that suffer from loss of identifiability. I...
Abstract This thesis is concerned with robust estimation of the parameters of statistical models. Al...
Unique parametrizations of models are very important for parameter interpretation and consistency of...
Identifiability is a necessary condition for the existence of consistent estimates for the parameter...
Testing for homogeneity in finite mixture models has been investigated by many authors. The asymptot...
The aim is to study the asymptotic behavior of estimators and tests for the components of identifiab...
The parameters of a finite mixture model cannot be consistently estimated when the data come from an...
Summary. Generalized linear models have become a standard technique in the statistical modelling too...
The parameters of a finite mixture model cannot be consistently estimated when the data come from an...
Finite mixture models provide a flexible framework to study unobserved entities and have arisen in m...
Generalized linear models have become a standard technique in the statistical modelling toolbox for ...
In this paper, we show under which conditions generalized finite mixture with underlying normal distr...
This thesis studies two types of research problems under finite mixture models. The first type is mi...
Finite normal mixture models are often used to model the data coming from a population which consist...
none2noRecently, finite mixture models have been used to model the distribution of the error terms i...
AbstractStatistical analyses commonly make use of models that suffer from loss of identifiability. I...
Abstract This thesis is concerned with robust estimation of the parameters of statistical models. Al...
Unique parametrizations of models are very important for parameter interpretation and consistency of...
Identifiability is a necessary condition for the existence of consistent estimates for the parameter...
Testing for homogeneity in finite mixture models has been investigated by many authors. The asymptot...
The aim is to study the asymptotic behavior of estimators and tests for the components of identifiab...
The parameters of a finite mixture model cannot be consistently estimated when the data come from an...
Summary. Generalized linear models have become a standard technique in the statistical modelling too...
The parameters of a finite mixture model cannot be consistently estimated when the data come from an...
Finite mixture models provide a flexible framework to study unobserved entities and have arisen in m...
Generalized linear models have become a standard technique in the statistical modelling toolbox for ...
In this paper, we show under which conditions generalized finite mixture with underlying normal distr...
This thesis studies two types of research problems under finite mixture models. The first type is mi...
Finite normal mixture models are often used to model the data coming from a population which consist...
none2noRecently, finite mixture models have been used to model the distribution of the error terms i...
AbstractStatistical analyses commonly make use of models that suffer from loss of identifiability. I...
Abstract This thesis is concerned with robust estimation of the parameters of statistical models. Al...
Unique parametrizations of models are very important for parameter interpretation and consistency of...