TY - JOUR
T1 - On the occasional exactness of the distributional transform approximation for direct Gaussian copula models with discrete margins
AU - Hughes, John
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/10
Y1 - 2021/10
N2 - The direct Gaussian copula model with discrete margins is appealing but poses computational challenges due to its intractable likelihood. We show that the distributional transform-based approximate likelihood is essentially exact for some variants of the model, and we propose a quantity that can be used to assess exactness for a given dataset.
AB - The direct Gaussian copula model with discrete margins is appealing but poses computational challenges due to its intractable likelihood. We show that the distributional transform-based approximate likelihood is essentially exact for some variants of the model, and we propose a quantity that can be used to assess exactness for a given dataset.
UR - http://www.scopus.com/inward/record.url?scp=85106492793&partnerID=8YFLogxK
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U2 - 10.1016/j.spl.2021.109159
DO - 10.1016/j.spl.2021.109159
M3 - Article
AN - SCOPUS:85106492793
SN - 0167-7152
VL - 177
JO - Statistics and Probability Letters
JF - Statistics and Probability Letters
M1 - 109159
ER -