Missing Variables in Bayesian Regression. II,

Abstract

The problem of parameter estimation in normal regression when some of the observations are missing was investigated. A Bayesian approach with vague prior distributions is taken. No assumption is made about the independent variables for which no observations are missing, but the missing components are assumed to be normally distributed with a mean that can depend on the other variables. Joint estimators of the parameters are obtained as the joint mode of the posterior distribution.

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1975
Accession Number
ADA021707

Entities

People

  • A. J. Scott
  • S. J. Press

Organizations

  • RAND Corporation

Tags

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Bayesian Networks
  • Data Acquisition
  • Estimators
  • Mathematical Models
  • Mathematics
  • Observation

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference