Name
Abstract | ||
|---|---|---|
Recently, there has been a considerable interest in finite mixture models with semi-/non-parametric component distributions. Identifiability of such model parameters is generally not obvious, and when it occurs, inference methods are rather specific to the mixture model under consideration. Hence, a generalization of the EM algorithm to semiparametric mixture models is proposed. The approach is methodological and can be applied to a wide class of semiparametric mixture models. The behavior of the proposed EM type estimators is studied numerically not only through several Monte-Carlo experiments but also through comparison with alternative methods existing in the literature. In addition to these numerical experiments, applications to real data are provided, showing that the estimation method behaves well, that it is fast and easy to be implemented. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.csda.2006.08.015 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
stochastic em algorithm,monte-carlo experiment,model parameter,mixture model,stochastic em,estimation method,em algorithm,semiparametric mixture model,proposed em type estimator,finite mixture model,semiparametric model,alternative method,considerable interest | Econometrics,Monte Carlo method,Parametric model,Expectation–maximization algorithm,Identifiability,Semiparametric model,Semiparametric regression,Statistics,Mixture model,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
51 | 11 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
13 | 2.11 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laurent Bordes | 1 | 36 | 4.37 |
| Didier Chauveau | 2 | 15 | 3.51 |
| Pierre Vandekerkhove | 3 | 13 | 2.11 |