Statistical Inference in Factor Analysis

Abstract

In this paper we discuss some methods of factor analysis. The entire discussion is centeredaround one general probability model. We consider some mathematical problemsof the model, such as whether certain kinds of observed data determine the model uniquely.We treat the statistical problems of estimation and tests of certain hypotheses. Forthese purposes the asymptotic distribution theory of some statistics is treated.The primary aim of this paper is to give a unified exposition of this part of factor analysisfrom the viewpoint of the mathematical statistician. The literature on factor analysisis scattered; moreover, the many papers and books have been written from many differentpoints of view. By confining ourselves to one model and by emphasizing statisticalinferences for this model we hope to present a clear picture to the statistician.The development given here is expected to point up features of model-building andstatistical inference that occur in other areas where statistical theories are being developed.For example, nearly all of the problems met in factor analysis are met in latentstructure analysis.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1956
Accession Number
AD1028617

Entities

People

  • Herman Rubin
  • Thomas G. Anderson

Organizations

  • Columbia University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Air Force
  • Analysis Of Variance
  • Asymptotic Normality
  • Correlation Analysis
  • Covariance
  • Data Science
  • Distribution Theory
  • Factor Analysis
  • Information Science
  • Military Research
  • New York
  • Normal Distribution
  • Normality
  • Probability
  • Statistical Inference
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Data Mining and Knowledge Discovery.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference