Universal piecewise linear regression of individual sequences: Lower bound

Georg C. Zeitler, Andrew Carl Singer, Suleyman S. Kozat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider universal piecewise linear regression of real valued bounded sequences under the squared loss function. In this setting, we present a lower bound on the regret of a universal sequential piecewise linear regressor compared to the best piecewise linear regressor that has access to the entire sequence in advance. This lower bound is tight in that it achieves the corresponding upper bound, suggesting a minmax optimality of the sequential regressor, for every individual bounded sequence.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
DOIs
StatePublished - Aug 6 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
ISSN (Print)1520-6149

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Keywords

  • Minimax methods
  • Piecewise linear approximation
  • Prediction methods
  • Regression
  • Universal

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Zeitler, G. C., Singer, A. C., & Kozat, S. S. (2007). Universal piecewise linear regression of individual sequences: Lower bound. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 [4217841] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 3). https://doi.org/10.1109/ICASSP.2007.366811