Review Articles

Empirical likelihood estimation in multivariate mixture models with repeated measurements

Yuejiao Fu ,

a Department of Mathematics and Statistics, York University, Toronto, Canada

Yukun Liu ,

b School of Statistics, East China Normal University, Shanghai, China

Hsiao-Hsuan Wang ,

a Department of Mathematics and Statistics, York University, Toronto, Canada

Xiaogang Wang

a Department of Mathematics and Statistics, York University, Toronto, Canada;c Institute of Data Science, Tsinghua University, Beijing, China

Pages 152-160 | Received 12 Nov. 2018, Accepted 07 Jun. 2019, Published online: 19 Jun. 2019,
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Multivariate mixtures are encountered in situations where the data are repeated or clustered measurements in the presence of heterogeneity among the observations with unknown proportions. In such situations, the main interest may be not only in estimating the component parameters, but also in obtaining reliable estimates of the mixing proportions. In this paper, we propose an empirical likelihood approach combined with a novel dimension reduction procedure for estimating parameters of a two-component multivariate mixture model. The performance of the new method is compared to fully parametric as well as almost nonparametric methods used in the literature.


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