### Abstract

It is generally assumed that the likelihood ratio statistic for testing the null hypothesis that data arise from a homoscedastic normal mixture distribution versus the alternative hypothesis that data arise from a heteroscedastic normal mixture distribution has an asymptotic χ^{2} reference distribution with degrees of freedom equal to the difference in the number of parameters being estimated under the alternative and null models under some regularity conditions. Simulations show that the χ^{2} reference distribution will give a reasonable approximation for the likelihood ratio test only when the sample size is 2000 or more and the mixture components are well separated when the restrictions suggested by Hathaway (Ann. Stat. 13:795-800, 1985) are imposed on the component variances to ensure that the likelihood is bounded under the alternative distribution. For small and medium sample sizes, parametric bootstrap tests appear to work well for determining whether data arise from a normal mixture with equal variances or a normal mixture with unequal variances.

Original language | English (US) |
---|---|

Pages (from-to) | 233-240 |

Number of pages | 8 |

Journal | Statistics and Computing |

Volume | 18 |

Issue number | 3 |

DOIs | |

Publication status | Published - Sep 1 2008 |

Externally published | Yes |

### Fingerprint

### Keywords

- Bootstrap
- EM algorithm
- Likelihood ratio test
- Normal mixture

### ASJC Scopus subject areas

- Theoretical Computer Science
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Computational Theory and Mathematics