General Constructive Theory of Parametric and Robust Data Smoothers.

Abstract

In relatively recent years, several algorithms for smoothing of time series have been proposed by statisticians. Some of the simpler such algorithms have been also applied in several engineering applications such as Image Processing. The smoothing problem and the implied objective have not been formalized and stated, however. This fact presents a serious handicap when different smoothing algorithms are to be compared in terms of their performance. In this paper, we take a fresh and daring approach to the whole smoothing problem. We formalize the problem as the extraction of a low entropy process from a high entropy process, and as a result we present a constructive theory of parametric and robust data smoothers. We claim that parametric data smoothers are analog-to-digital converters, and that robust data smoothers are stochastic such converters. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1980
Accession Number
ADA096004

Entities

People

  • P. Papantoni-kazakos

Organizations

  • University of Connecticut

Tags

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computer Science
  • Converters
  • Data Analysis
  • Digital Data
  • Electrical Engineering
  • Engineering
  • Ergodic Processes
  • Extraction
  • Finite Alphabet
  • Information Science
  • Information Theory
  • Probability
  • Scientific Research
  • Stationary Processes
  • Stochastic Processes

Fields of Study

  • Computer science
  • Mathematics

Readers

  • Educational Psychology
  • Image Processing and Computer Vision.
  • Statistical inference.