Penalized maximum likelihood estimator for mixture of von Mises–Fisher distributions View Full Text


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Article Info

DATE

2022-06-15

AUTHORS

Tin Lok James Ng

ABSTRACT

The von Mises–Fisher distribution is one of the most widely used probability distributions to describe directional data. Finite mixtures of von Mises–Fisher distributions have found numerous applications. However, the likelihood function for the finite mixture of von Mises–Fisher distributions is unbounded and consequently the maximum likelihood estimation is not well defined. To address the problem of likelihood degeneracy, we consider a penalized maximum likelihood approach whereby a penalty function is incorporated. We prove strong consistency of the resulting estimator. An Expectation–Maximization algorithm for the penalized likelihood function is developed and experiments are performed to examine its performance. More... »

PAGES

1-23

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00184-022-00867-0

DOI

http://dx.doi.org/10.1007/s00184-022-00867-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1148692488


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