On Bagging and Estimation in Multivariate Mixtures

Two bagging approaches, say 1 2 n-out-of-n without replacement (subagging) and n-out-of-n with replacement (bagging) have been applied in the problem of estimation of the parameters in a multivariate mixture model. It has been observed by Monte Carlo simulations and a real data example, that both bagging methods have improved the standard deviation of the maximum likelihood estimator of the mixing proportion, whilst the absolute bias increased slightly. In estimating the component distributions, bagging could increase the root mean integrated squared error when estimating the most probable component.