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A note on the article ‘Inference for multivariate normal mixtures’ by J. Chen and X. Tan

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  • Alexandrovich, Grigory

Abstract

The current note discusses the consistency proof for the penalized maximum likelihood estimator of a Gaussian mixture from the paper ‘Inference for multivariate normal mixtures’ by J. Chen and X. Tan. A soft spot in that proof is identified and a rigorous alternative proof based on a uniform law of iterated logarithm is given.

Suggested Citation

  • Alexandrovich, Grigory, 2014. "A note on the article ‘Inference for multivariate normal mixtures’ by J. Chen and X. Tan," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 245-248.
    • Handle: RePEc:eee:jmvana:v:129:y:2014:i:c:p:245-248
    DOI: 10.1016/j.jmva.2014.04.008
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    References listed on IDEAS

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    1. Gabriela Ciuperca & Andrea Ridolfi & Jérôme Idier, 2003. "Penalized Maximum Likelihood Estimator for Normal Mixtures," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 45-59, March.
    2. Chen, Jiahua & Tan, Xianming, 2009. "Inference for multivariate normal mixtures," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1367-1383, August.
    3. Kentaro Tanaka, 2009. "Strong Consistency of the Maximum Likelihood Estimator for Finite Mixtures of Location–Scale Distributions When Penalty is Imposed on the Ratios of the Scale Parameters," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 171-184, March.
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    Cited by:

    1. Paul Schrimpf & Michio Suzuki & Hiroyuki Kasahara, 2015. "Identification and Estimation of Production Function with Unobserved Heterogeneity," 2015 Meeting Papers 924, Society for Economic Dynamics.
    2. Yu Hao & Hiroyuki Kasahara, 2022. "Testing the Number of Components in Finite Mixture Normal Regression Model with Panel Data," Papers 2210.02824, arXiv.org, revised Jun 2023.

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