Add to ORKGWe consider data generating mechanisms which can be represented as mixtures of finitely many regression or autoregression models. We propose nonparametric estimators for the functions characterizing the various mixture components based on a local quasi maximum likelihood approach and prove their consistency. We present an EM algorithm for calculating the estimates numerically which is mainly based on iteratively applying common local smoothers and discuss its convergence properties
We consider a novel class of non-linear models for time series analysis based on mixtures of local a...
Abstract. Recently several authors considered finite mixture models with semi-/non-parametric compon...
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a fi...
We consider data generating mechanisms which can be represented as mixtures of finitely many regress...
We consider data generating mechanisms which can be represented as mixtures of finitely many regress...
We describe and investigate a data-driven procedure for obtaining parsimonious mixture model estimat...
Abstract Suppose independent observations X i , i = 1, . . . , n are observed from a mixture model f...
The aim of this paper is to provide simple nonparametric methods to estimate finitemixture models fr...
International audienceIn this paper we are interested in estimating the number of components of a mi...
This paper provides methods to estimate finite mixtures from data with repeated measurements non-par...
We present a split and merge EM (SMEM) algorithm to overcome the local maximum problem in parameter ...
In this paper, we reconsider the mixture vector autoregressive model, which was proposed in the lite...
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a fi...
In this paper, we study a class of semiparametric mixtures of regression models, in which the regres...
The maximum likelihood estimation in the finite mixture of distributions setting is an ill-posed pro...
We consider a novel class of non-linear models for time series analysis based on mixtures of local a...
Abstract. Recently several authors considered finite mixture models with semi-/non-parametric compon...
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a fi...
We consider data generating mechanisms which can be represented as mixtures of finitely many regress...
We consider data generating mechanisms which can be represented as mixtures of finitely many regress...
We describe and investigate a data-driven procedure for obtaining parsimonious mixture model estimat...
Abstract Suppose independent observations X i , i = 1, . . . , n are observed from a mixture model f...
The aim of this paper is to provide simple nonparametric methods to estimate finitemixture models fr...
International audienceIn this paper we are interested in estimating the number of components of a mi...
This paper provides methods to estimate finite mixtures from data with repeated measurements non-par...
We present a split and merge EM (SMEM) algorithm to overcome the local maximum problem in parameter ...
In this paper, we reconsider the mixture vector autoregressive model, which was proposed in the lite...
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a fi...
In this paper, we study a class of semiparametric mixtures of regression models, in which the regres...
The maximum likelihood estimation in the finite mixture of distributions setting is an ill-posed pro...
We consider a novel class of non-linear models for time series analysis based on mixtures of local a...
Abstract. Recently several authors considered finite mixture models with semi-/non-parametric compon...
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a fi...