NONPARAMETRIC ESTIMATION ALGORITHMS BASED ON INPUT QUANTIZATION.

Chung Chieh Lee*, L. A. Longley

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The estimation of a parameter of a white discrete-time process with arbitrary statistical distribution is considered, using quantized samples. Because of the quantization the necessary statistical modeling is simplified to the measurement of a few parameters. Under the assumption that the parameter space is a small interval, a locally optimum estimator (LOE) is derived. It is shown that this estimator has a desirable parallel structure for implementation by simple digital hardware. The idea is then extended to the case of large parameter space for which a G-estimator consisting of an array of identical LOEs is presented. To analyze the performance of this scheme, the estimation of the location parameter of a continuous, unimodal, and symmetric distribution is studied. In this case it is proved that the G-estimator extends the optimality of a single LOE to the larger parameter space.

Original languageEnglish (US)
Pages (from-to)682-688
Number of pages7
JournalIEEE Transactions on Information Theory
VolumeIT-31
Issue number5
StatePublished - Sep 1 1985

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering

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