Rational Maximum Likelihood Estimators of Kronecker Covariance Matrices
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929476326129664 |
|---|---|
| author | Drton, Mathias Grosdos, Alexandros McCormack, Andrew |
| author_facet | Drton, Mathias Grosdos, Alexandros McCormack, Andrew |
| contents | As is the case for many curved exponential families, the computation of maximum likelihood estimates in a multivariate normal model with a Kronecker covariance structure is typically carried out with an iterative algorithm, specifically, a block-coordinate ascent algorithm. In this article we highlight a setting, specified by a coprime relationship between the sample size and dimension of the Kronecker factors, where the likelihood equations have algebraic degree one and an explicit, easy-to-evaluate rational formula for the maximum likelihood estimator can be found. A partial converse of this result is provided that shows that outside of the aforementioned special setting and for large sample sizes, examples of data sets can be constructed for which the degree of the likelihood equations is larger than one. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08280 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rational Maximum Likelihood Estimators of Kronecker Covariance Matrices Drton, Mathias Grosdos, Alexandros McCormack, Andrew Statistics Theory 62R01 As is the case for many curved exponential families, the computation of maximum likelihood estimates in a multivariate normal model with a Kronecker covariance structure is typically carried out with an iterative algorithm, specifically, a block-coordinate ascent algorithm. In this article we highlight a setting, specified by a coprime relationship between the sample size and dimension of the Kronecker factors, where the likelihood equations have algebraic degree one and an explicit, easy-to-evaluate rational formula for the maximum likelihood estimator can be found. A partial converse of this result is provided that shows that outside of the aforementioned special setting and for large sample sizes, examples of data sets can be constructed for which the degree of the likelihood equations is larger than one. |
| title | Rational Maximum Likelihood Estimators of Kronecker Covariance Matrices |
| topic | Statistics Theory 62R01 |
| url | https://arxiv.org/abs/2401.08280 |