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Maximum Likelihood Estimation in the Multivariate and Matrix Variate Symmetric Laplace Distributions through Group Actions

Published: October 28, 2025 | arXiv ID: 2510.24863v2

By: Pooja Yadav, Tanuja Srivastava

Potential Business Impact:

Finds best math models for complex data.

Business Areas:
A/B Testing Data and Analytics

In this paper, we study the maximum likelihood estimation of the parameters of the multivariate and matrix variate symmetric Laplace distributions through group actions. The multivariate and matrix variate symmetric Laplace distributions are not in the exponential family of distributions. We relate the maximum likelihood estimation problems of these distributions to norm minimization over a group and build a correspondence between stability of data with respect to the group action and the properties of the likelihood function.

Country of Origin
🇮🇳 India

Page Count
26 pages

Category
Mathematics:
Statistics Theory