Likelihood inference in the errors-in-variables model
Journal of Multivariate Analysis
Minumum Hellinger distance estimation for Poisson mixtures
Computational Statistics & Data Analysis
Robust estimation of mixture complexity for count data
Computational Statistics & Data Analysis
Simulated minimum Hellinger distance estimation of stochastic volatility models
Computational Statistics & Data Analysis
Minimum Hellinger distance estimation in a two-sample semiparametric model
Journal of Multivariate Analysis
Minimum distance estimation in a finite mixture regression model
Journal of Multivariate Analysis
| Hi-index | 0.00 |
Minimum distance techniques have become increasingly important tools for solving statistical estimation and inference problems. In particular, the successful application of the Hellinger distance approach to fully parametric models is well known. The corresponding optimal estimators, known as minimum Hellinger distance estimators, achieve efficiency at the model density and simultaneously possess excellent robustness properties. For statistical models that are semiparametric, in that they have a potentially infinite dimensional unknown nuisance parameter, minimum distance methods have not been fully studied. In this paper, we extend the Hellinger distance approach to general semiparametric models and study minimum Hellinger distance estimators for semiparametric models. Asymptotic properties such as consistency, asymptotic normality, efficiency and adaptivity of the proposed estimators are investigated. Small sample and robustness properties of the proposed estimators are also examined using a Monte Carlo study. Two real data examples are analyzed as well.