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Maximum likelihood estimation of the Latent Class Model through model boundary decomposition

Abstract:

The Expectation-Maximization (EM) algorithm is routinely used for the maximum likelihood estimation in the latent class analysis. However, the EM algorithm comes with no guarantees of reaching the global optimum. We study the geometry of the latent class model in order to understand the behavior of the maximum likelihood estimator. In particular, we characterize the boundary stratification of the binary latent class model with a binary hidden variable. For small models, such as for three bina...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Authors


Cervantes, H More by this author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Oxford college:
Jesus College
ORCID:
0000-0002-9341-1313
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Publisher:
Paul V. Galvin Library/Illinois Institute of Technology Publisher's website
Journal:
Journal of Algebraic Statistics Journal website
Volume:
10
Issue:
1
Pages:
51-84
Publication date:
2019-04-10
Acceptance date:
2018-10-30
EISSN:
1309-3452
Pubs id:
pubs:935374
URN:
uri:301ecb1e-2c67-458c-818f-6b689419e36c
UUID:
uuid:301ecb1e-2c67-458c-818f-6b689419e36c
Local pid:
pubs:935374

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