Partial likelihood for real-time signal processing

Tulay Adali, M. Kemal Sonmez, Xiao Liu

Research output: Contribution to journalConference article

1 Scopus citations

Abstract

We introduce a unified statistical framework for real-time signal processing with neural networks by using a recent extension of maximum likelihood (ML) estimation, partial likelihood (PL) estimation theory, which allows for (i) dependent observations, and (ii) processing of data using only the information that is available at the time of processing. For a general neural network conditional distribution model, we establish a fundamental information-theoretic relationship for PL estimation, and obtain large sample properties of PL for the general case of dependent observations. We consider applications of PL to prediction and channel equalization.

Original languageEnglish (US)
Pages (from-to)3561-3564
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume6
StatePublished - Jan 1 1996
Externally publishedYes
EventProceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA
Duration: May 7 1996May 10 1996

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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