Technical Program

Paper Detail

Paper IDE.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).

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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