Approaches to Inverse Linear Regression. Revision.

Abstract

Many measurement problems can be formulated as follows: First, a certain linear relationship between two variables is to be estimated by using pairs of input and output data; thereafter, the value of an unknown input variable is to be estimated given an observation of the corresponding output variable. This problem is often referred to as inverse regression or discrimination. In this paper first non-Bayesian approaches to the problem, thereafter the Bayesian approach by Hoadley are presented. Third, a Bayesian approach by Avenhaus and Jewell is discussed which uses the ideas of credibility theory. Finally, a new Bayesian approach is presented. The advantages and disadvantages of the various approaches are put together. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1980
Accession Number
ADA093787

Entities

People

  • E. Hoepfinger
  • R. Avenhaus
  • W. S. Jewell

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Bayesian Inference
  • Bayesian Networks
  • Calibration
  • Coefficients
  • Confidence Limits
  • Data Science
  • Distribution Functions
  • Estimators
  • Information Science
  • Measurement
  • New York
  • Operations Research
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Statistical Inference
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms