TY - GEN
T1 - Estimation with Quantized Parameter Side-Information
AU - Wetterwald, Mathis
AU - Shayevitz, Ofer
AU - Wigger, Michèle
N1 - Publisher Copyright: © 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - This paper presents upper and lower bounds on the minimax risk of a parameter estimation problem where the estimator not only observes independent and identically distributed observations but also a quantized version of the parameter, where the quantization has a limitation on the number of levels, but can be optimized otherwise. Our upper and lower bounds have similar decay rates for large number of observation samples and quantization levels.
AB - This paper presents upper and lower bounds on the minimax risk of a parameter estimation problem where the estimator not only observes independent and identically distributed observations but also a quantized version of the parameter, where the quantization has a limitation on the number of levels, but can be optimized otherwise. Our upper and lower bounds have similar decay rates for large number of observation samples and quantization levels.
UR - https://www.scopus.com/pages/publications/105049088775
U2 - 10.1109/ISIT62367.2026.11653704
DO - 10.1109/ISIT62367.2026.11653704
M3 - Conference contribution
T3 - IEEE International Symposium on Information Theory - Proceedings
BT - ISIT 2026 - 2026 IEEE International Symposium on Information Theory, Symposium Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2026 IEEE International Symposium on Information Theory, ISIT 2026
Y2 - 28 June 2026 through 3 July 2026
ER -