
doi: 10.1002/cjs.10106
Summary: Testing homogeneity is a fundamental problem in finite mixture models. It has been investigated by many researchers and most of the existing works have focused on the univariate case. In this article, the authors extend the use of the EM-test for testing homogeneity to multivariate mixture models. They show that the EM-test statistic asymptotically has the same distribution as a certain transformation of a single multivariate normal vector. On the basis of this result, they suggest a resampling procedure to approximate the P-value of the EM-test. Simulation studies show that the EM-test has accurate type I errors and adequate power, and is more powerful and computationally efficient than the bootstrap likelihood ratio test. Two real data sets are analysed to illustrate the application of our theoretical results.
Asymptotic properties of parametric tests, Bootstrap, jackknife and other resampling methods, Computational problems in statistics, EM-test, likelihood ratio test, Hypothesis testing in multivariate analysis, limiting distribution
Asymptotic properties of parametric tests, Bootstrap, jackknife and other resampling methods, Computational problems in statistics, EM-test, likelihood ratio test, Hypothesis testing in multivariate analysis, limiting distribution
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 14 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
