On Linear Estimators for some Stable Vectors
Abstract
We consider the estimation problem for jointly stable random variables. Under two specific dependency models: a linear transformation of two independent stable variables and a sub-Gaussian symmetric $α$-stable (S$α$S) vector, we show that the conditional mean estimator is linear in both cases. Moreover, we find dispersion optimal linear estimators. Interestingly, for the sub-Gaussian (S$α$S) vector, both estimators are identical generalizing the well-known Gaussian result of the conditional mean being the best linear minimum-mean square estimator.
Fields of Study & Subjects
Statistical Methods and Inference
Financial Risk and Volatility Modeling
Risk and Portfolio Optimization
Publication details
- Journal / source
- 2026 IEEE International Symposium on Information Theory (ISIT)
- Publisher
- IEEE
- Pages
- 1–6
- Publication date
- 1 June 2026