Multivariate Generalizations of Student's t-Distribution

Abstract

In the process of developing a conditionally dependent item-response theory model, we were confronted with the problem of modeling an underlying multivariate normal (MVN) response process with general correlation among the items. Without the assumption of conditional of conditional independence, for which the underlying MVN cdf takes on comparatively simple forms, and can be numerically evaluated using existing reduction formulae, our task required the development of a computationally fast, tractable and accurate approximation of multivariate normal orthant probabilities for general correlation. The focus of our previous technical reports have provided such a method, based on Clark's approximation of the moments of n correlated random normal variables. The major thrust of this work continues in the area of applying this algorithm to problems in item-response theory (IRT). The focus of this report, however, is on the application of our previous results to another problem in statistics; namely, the generation of simultaneous confidence bounds for multiple correlated comparisons. In this report the authors illustrate how the results we have obtained in the IRT context can be applied to simultaneous statistical inference problems of various kinds.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1990
Accession Number
ADA229128

Entities

People

  • Donald Hedeker
  • R. D. Bock
  • Robert D. Gibbons

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Coefficients
  • Confidence Limits
  • Data Science
  • Education
  • Equations
  • Fertility
  • Illinois
  • Information Science
  • Probability
  • Regression Analysis
  • Security
  • Statistical Algorithms
  • Statistical Inference
  • Test And Evaluation
  • United States

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Psychometric Testing or Psychological Assessment.
  • Regression Analysis.

Technology Areas

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