466 citations · 553 across the 25 of their papers we have counts for
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A generalized AIC for models with singularities and boundaries
Jonathan D. Mitchell, Elizabeth S. Allman, John A. Rhodes
The Akaike information criterion (AIC) is a common tool for model selection. It is frequently used in violation of regularity conditions at parameter space singularities and bounda…
Parameter Identifiability of a Multitype Pure-Birth Model of Speciation
Dakota Dragomir, Elizabeth S. Allman, John A. Rhodes
Diversification models describe the random growth of evolutionary trees, modeling the historical relationships of species through speciation and extinction events. One class of suc…
Hypothesis testing near singularities and boundaries
Jonathan D. Mitchell, Elizabeth S. Allman, John A. Rhodes
The likelihood ratio statistic, with its asymptotic distribution at regular model points, is often used for hypothesis testing. At model singularities and boundaries, however…
Maximum likelihood estimation of the Latent Class Model through model boundary decomposition
Elizabeth S. Allman, Hector Baños Cervantes, Robin Evans +5
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…
Parameter identifiability of discrete Bayesian networks with hidden variables
Elizabeth S. Allman, John A. Rhodes, Elena Stanghellini +1
Identifiability of parameters is an essential property for a statistical model to be useful in most settings. However, establishing parameter identifiability for Bayesian networks…
Parameter identifiability in a class of random graph mixture models
Elizabeth S. Allman, Catherine Matias, John A. Rhodes
We prove identifiability of parameters for a broad class of random graph mixture models. These models are characterized by a partition of the set of graph nodes into latent (unobse…