UC Riverside

In this article, the label switching problem and the importance of solving it are discussed for frequentist mixture models if a simulation study is used to evaluate the performance of mixture model estimators. Two effective labelling methods are proposed by using true label for each observation. The empirical studies demonstrate that the new proposed methods work well and provide better results than the rule of thumb method of order constraint labelling. In addition, a Monte Carlo study also demonstrates that simple order constraint labelling can sometimes produce severely biased, and possibly meaningless, estimated bias and standard errors.