Titterington proposed a recursive parameter estimation algorithm for finite mixture models. However, due to the well known problem of singularities and multiple maximum, minimum and saddle points that are possible on the likelihood surfaces, convergence analysis has seldom been made in the past years. In this paper, under mild conditions, we show the global convergence of Titterington's recursive estimator and its MAP variant for mixture models of full regular exponential family.Recursive estimation Incomplete data Mixture model Regular exponential family Almost sure convergence Stochastic approximation
Efficient probability density function estimation is of primary interest in statistics. A popular ap...
The parameters of a finite mixture model cannot be consistently estimated when the data come from an...
Finite mixture distributions are used in applications because of their ability to support heterogene...
We consider a new recursive algorithm for parameter estimation from an independent incomplete data s...
this paper. Our experimental evidence suggests that setting j ? 1 results in a more effective update...
A recursive algorithm is proposed for estimation of parameters in mixture models, where the observat...
IEEE Computer Society Abstract—There are two open problems when finite mixture densities are used to...
Mixture distributions have, for many years, been used in a wide range of classical statistical probl...
International audienceEstimators derived from the expectation‐maximization (EM) algorithm are not ro...
We revisit the classical problem of deriving convergence rates for the maximum likelihood estimator ...
model approximation, exponential family. A natural way of beating the complexity of statistical para...
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a fi...
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a fi...
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a fi...
We consider a class of stochastic approximation (SA) algorithms for solving a system of estimating e...
Efficient probability density function estimation is of primary interest in statistics. A popular ap...
The parameters of a finite mixture model cannot be consistently estimated when the data come from an...
Finite mixture distributions are used in applications because of their ability to support heterogene...
We consider a new recursive algorithm for parameter estimation from an independent incomplete data s...
this paper. Our experimental evidence suggests that setting j ? 1 results in a more effective update...
A recursive algorithm is proposed for estimation of parameters in mixture models, where the observat...
IEEE Computer Society Abstract—There are two open problems when finite mixture densities are used to...
Mixture distributions have, for many years, been used in a wide range of classical statistical probl...
International audienceEstimators derived from the expectation‐maximization (EM) algorithm are not ro...
We revisit the classical problem of deriving convergence rates for the maximum likelihood estimator ...
model approximation, exponential family. A natural way of beating the complexity of statistical para...
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a fi...
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a fi...
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a fi...
We consider a class of stochastic approximation (SA) algorithms for solving a system of estimating e...
Efficient probability density function estimation is of primary interest in statistics. A popular ap...
The parameters of a finite mixture model cannot be consistently estimated when the data come from an...
Finite mixture distributions are used in applications because of their ability to support heterogene...