Maximum Tsallis Entropy Distributions for Robust and Efficient Sparse Learning from Correlated Data
This paper addresses the limitations of Gaussian distribution assumptions in statistical sparse learning, particularly in modeling correlated and heterogeneous data. Conventional Gaussian models often lack robustness towards outliers and underlying distribution assumptions. To overcome these limitations, we propose the use of the $q$Gaussian distribution, derived from Tsallis entropy maximization, as a robust alternative. This is notably relevant in biostatistics, where the presence of correlated observations and heterogeneity, such as in genetic and longitudinal studies, is prevalent. Our contributions include modeling of correlated data through the re-derived multivariate probability de...
| Authors | Kai Yang, Masoud Asgharian, Celia M. T. Greenwood |
|---|---|
| Primary category | math.OC |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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