Minimum Hellinger Distance Estimation for Finite Mixture Models
研究了当混合模型中各成分密度未知但接近某参数族时，最小Hellinger距离估计量的渐近有效性和稳健性，并提出了新算法和自适应密度估计。
Abstract Minimum Hellinger distance estimates are considered for finite mixture models when the exact forms of the component densities are unknown in detail but are thought to be close to members of some parametric family. Minimum Hellinger distance estimates are asymptotically efficient if the data come from a member of the parametric family and are robust to certain departures from the parametric family. A new algorithm is introduced that is similar to the EM algorithm a specialized adaptive density estimate is also introduced. Standard measures of robustness are discussed some difficulties are noted. The robustness and asymptotic efficiency of the estimators are illustrated using simulations.