In many applications, it is important to find the mixture with fewest number of components,
known as the mixture complexity, that provides a satisfactory fit to the data. This article …

For finite mixtures, consistent estimation of unknown number of components, called mixture
complexity, is considered based on a random sample of counts, when the exact form of …

Beran (1977) showed that, under certain restrictive conditions, the minimum distance
estimator based on the Hellinger distance (MHDE) between a projection model density and …

Estimation of the number of mixture components (k) is an unsolved problem. Available
methods for estimation of k include bootstrapping the likelihood ratio test statistic and …

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 …

Inference for mixture models based on likelihood estimates suffers from lack of robustness.
The presence of a few spurious observations may lead to incorrect decisions. In this paper …

The estimation of mixing proportions in the mixture model is discussed, with emphasis on
the mixture of two normal components with all five parameters unknown. Simulations are …

Abstract Model identification is an essential but often neglected component of sound
statistical inference. In mixture analysis, data are assumed to be sampled from a distribution …

A finite mixture is a distribution where a given observation can come from any of a finite set
of components. That is, the density of the random variable X is of the form f (x)= π1f 1 (x)+ …

Efficiency and robustness are two fundamental concepts in parametric estimation problems.
It was long thought that there was an inherent contradiction between the aims of achieving …