EFFECT OF NON-NORMALITY ON INFERENCES ABOUT VARIANCE COMPONENTS.

Abstract

Bayesian methods are used to analyse the one-way random effect model y sub ij = mu + a sub i + e sub ij in which e sub ij are assumed normal, N(0, sigma squared sub e), and a sub i are assumed to have a mixture of two normals, .95N(-.05phi(k-1) sigma, sigma squared) + .05N(.95phi(k-1) sigma, k squared, sigma squared). It is shown that for moderately sized sample, inferences regarding sigma sigma squared sub e = var(e sub ij) are insensitive but those of sigma squared sub a = var(a sub i) are very sensitive to changes of the two non-normality parameters (k, phi). (Author)

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1968
Accession Number
AD0686295

Entities

People

  • G. C. Tiao
  • M. M. Ali

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Computing-Related Activities
  • Data Science
  • Information Science
  • Interdisciplinary Science
  • Mathematical Analysis
  • Mathematics
  • Normality

Readers

  • Analytical Mechanics
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference