Maximum Likelihood Thresholds of Generic Linear Concentration Models
Abstract
Abstract. The maximum likelihood threshold of a statistical model is the minimum number of datapoints required to fit the model via maximum likelihood estimation. In this paper, we determine the maximum likelihood thresholds of generic linear concentration models. This turns out to be the number that one might expect from a naive dimension count, which is nontrivial to prove, given that the maximum likelihood threshold is a semi-algebraic concept. We also describe geometrically how a linear concentration model can fail to exhibit this generic behavior.
Fields of Study & Subjects
Bayesian Methods and Mixture Models
Statistical Methods and Bayesian Inference
Publication details
- Journal / source
- SIAM Journal on Applied Algebra and Geometry
- Volume
- 10
- Issue
- 3
- Pages
- 603–620
- Publication date
- 17 August 2026
- Language
- EN