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International Journal of Statistics and Applied Mathematics

2021, Vol. 6, Issue 3, Part A

Maximum likelihood estimation for multivariate normal distribution with hierarchical missing data


Author(s): Jianqi Yu and Xiang Wang

Abstract: Closed forms are obtained for the maximum likelihood estimators (MLE) of the mean vector and the covariance matrix of a multivariate normal model with a hierarchical missing pattern. According to the missing pattern, the likelihood function is decomposed as product of several independent normal and conditional normal likelihood functions. The original parameters are transformed into a new set of parameters whose MLE are easy to derive. Since the MLE are invariant, the MLE of the original parameters are derived using the inverse transformation.

DOI: 10.22271/maths.2021.v6.i3a.681

Pages: 16-19 | Views: 157 | Downloads: 13

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How to cite this article:
Jianqi Yu, Xiang Wang. Maximum likelihood estimation for multivariate normal distribution with hierarchical missing data. Int J Stat Appl Math 2021;6(3):16-19. DOI: 10.22271/maths.2021.v6.i3a.681
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