Add to ORKGThe article presents an introduction to the theory of finite mixture distributions, discusses the ways of procedure in selecting the number and type of component distributions, the methods of assessing the initial values, and proposal of the procedure of estimating the parameters. The proposed procedure uses min.k/max.k (for k=1, 3, 6) and 0.5/1.5/average methods to select initial values for a numerical procedure (EM algorithm + Newton's method) allowing to calculate the extremum of the likelihood function. If at least two identical solutions for the extreme values are not obtained, the multistart method should additionally be applied
Finite Mixture models are a state-of-the-art technique of segmentation. Next tosegmenting consumers ...
Finite mixture models have found use in the analysis of high dimensional data such as result from mi...
Finite mixtures of probability distributions may be successfully used in the modeling of probability...
The aim of the presented study was to show an applicability of finite mixture distribution approach ...
textabstractFinite mixture distributions are a weighted average of a ¯nite number of distributions. ...
Mixture models occur in numerous settings including random and fixed effects models, clustering, dec...
The creation and maintenance of complex forest structures has become an important forestry objective...
The important role of finite mixture models in the statistical analysis of data is underscored by th...
Finite mixtures of distributions have provided a mathematical-based approach to the statistical mode...
Optimisation of distribution parameters is a very common problem. There are many sorts of distributi...
Mixture distributions and models are useful methods of describing data that cannot be estimated with...
This thesis deals with classification based on mixture models, mainly on models finite normal. At fi...
Mixture models occur in numerous settings including random and fixed effects models, clustering, dec...
Finite mixture models are increasingly used to model heterogeneous data in various important practic...
AbstractIn this paper we compare (numerically) two approaches to the estimation of the parameters of...
Finite Mixture models are a state-of-the-art technique of segmentation. Next tosegmenting consumers ...
Finite mixture models have found use in the analysis of high dimensional data such as result from mi...
Finite mixtures of probability distributions may be successfully used in the modeling of probability...
The aim of the presented study was to show an applicability of finite mixture distribution approach ...
textabstractFinite mixture distributions are a weighted average of a ¯nite number of distributions. ...
Mixture models occur in numerous settings including random and fixed effects models, clustering, dec...
The creation and maintenance of complex forest structures has become an important forestry objective...
The important role of finite mixture models in the statistical analysis of data is underscored by th...
Finite mixtures of distributions have provided a mathematical-based approach to the statistical mode...
Optimisation of distribution parameters is a very common problem. There are many sorts of distributi...
Mixture distributions and models are useful methods of describing data that cannot be estimated with...
This thesis deals with classification based on mixture models, mainly on models finite normal. At fi...
Mixture models occur in numerous settings including random and fixed effects models, clustering, dec...
Finite mixture models are increasingly used to model heterogeneous data in various important practic...
AbstractIn this paper we compare (numerically) two approaches to the estimation of the parameters of...
Finite Mixture models are a state-of-the-art technique of segmentation. Next tosegmenting consumers ...
Finite mixture models have found use in the analysis of high dimensional data such as result from mi...
Finite mixtures of probability distributions may be successfully used in the modeling of probability...