A Better Least-Squares Method when Both Variables Have Uncertainties

Abstract

The generalized least-squares problem, in which an observation vector satisfies a set of equations that may be nonlinear and implicit, and all components may be subject to errors, can be solved as a constrained minimization problem. When the analysis is specialized to the important case of one dimensional curve fitting to measurements where both variables contain errors, it becomes similar to the effective variance method. A standard least-squares computer program can be used to apply the new method; the results are superior to these of the effective variance technique. A simple geometrical construction illustrates the principles of the new method.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1984
Accession Number
ADA204955

Entities

People

  • Matthew Lybanon

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Availability
  • Classification
  • Computer Programs
  • Computers
  • Construction
  • Convergence
  • Curve Fitting
  • Data Analysis
  • Data Science
  • Equations
  • Iterations
  • Least Squares Method
  • Measurement
  • Security
  • Standards
  • Uncertainty

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Systems Analysis and Design