2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | E.3.6 | ||
| Paper Title | Linear Models are Most Favorable among Generalized Linear Models | ||
| Authors | Kuan-Yun Lee, Thomas A. Courtade, UC Berkeley, United States | ||
| Session | E.3: Estimation Theory | ||
| Presentation | Lecture | ||
| Track | Detection and Estimation | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | We establish a nonasymptotic lower bound on the $L_2$ minimax risk for a class of generalized linear models. It is further shown that the minimax risk for the canonical linear model matches this lower bound up to a universal constant. Therefore, the canonical linear model may be regarded as most favorable among the considered class of generalized linear models (in terms of minimax risk). The proof makes use of an information-theoretic Bayesian Cramér-Rao bound for log-concave priors, established by Aras et al. (2019). | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
