An EM algorithm for fitting mixtures of autoregressions of low order is constructed and the properties of the estimators are explored on simulated and real datasets. The mixture model incorporates a component with an improper density, which is intended for outliers. The model is proposed as an alternative to the search for the order of a single-component autoregression. The methods can be adapted to other patterns of dependence in panel data. An application to the monthly records of income of the outlets of a retail company is presented
Mixture autoregressive (MAR) Model is a mixture of Gaussian autoregressive (AR) components. The...
Mixture periodic autoregressive models are introduced to fit periodic time series with asymmetric or...
The aim of the paper is to go beyond the detection of outliers in multivariate time series, and to f...
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...
The paper is framed within the literature around Louis’ identity for the observed information matrix...
International audienceIn this paper we are interested in estimating the number of components of a mi...
This paper studies estimation in panel vector autoregression (VAR) under cross-sectional dependence....
Repeated count data showing overdispersion are commonly analysed by using a Poisson model with varyi...
In this article the effects of mixtures of two normal distributions on the fraction nonconforming ar...
Mixtures of factor analyzers enable model-based density estimation to be undertaken for high-dimensi...
this paper use consider the problem of providing standard errors of the component means in normal mi...
In a panel data model with random effects, when autocorrelation in the error is considered, (Gaussia...
The existing methods for tting mixture regression models assume a normal dis-tribution for error and...
In this paper, we reconsider the mixture vector autoregressive model, which was proposed in the lite...
Mixture autoregressive (MAR) Model is a mixture of Gaussian autoregressive (AR) components. The...
Mixture periodic autoregressive models are introduced to fit periodic time series with asymmetric or...
The aim of the paper is to go beyond the detection of outliers in multivariate time series, and to f...
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...
The paper is framed within the literature around Louis’ identity for the observed information matrix...
International audienceIn this paper we are interested in estimating the number of components of a mi...
This paper studies estimation in panel vector autoregression (VAR) under cross-sectional dependence....
Repeated count data showing overdispersion are commonly analysed by using a Poisson model with varyi...
In this article the effects of mixtures of two normal distributions on the fraction nonconforming ar...
Mixtures of factor analyzers enable model-based density estimation to be undertaken for high-dimensi...
this paper use consider the problem of providing standard errors of the component means in normal mi...
In a panel data model with random effects, when autocorrelation in the error is considered, (Gaussia...
The existing methods for tting mixture regression models assume a normal dis-tribution for error and...
In this paper, we reconsider the mixture vector autoregressive model, which was proposed in the lite...
Mixture autoregressive (MAR) Model is a mixture of Gaussian autoregressive (AR) components. The...
Mixture periodic autoregressive models are introduced to fit periodic time series with asymmetric or...
The aim of the paper is to go beyond the detection of outliers in multivariate time series, and to f...