Statistical Modeling of Bivariate Data.

Abstract

A technique for modeling bivariate data that is based on the theory of orthogonal expansions in a separable Hilbert space is examined. A new nonparametric density estimation procedure is developed using an information criterion and is shown to be equivalent to least squares estimation of a density when the criterion function is computed with respect to the empirical distribution function. Computer programs are presented that implement the procedure for the univariate and bivariate cases. Examples utilizing these programs are given and comparisons made to existing density estimation techniques. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1982
Accession Number
ADA119915

Entities

People

  • Terry Joe Woodfield

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • Biomedical
  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Computer Programs
  • Computers
  • Data Mining
  • Data Science
  • Information Processing
  • Information Science
  • Operating Systems
  • Random Variables
  • Regression Analysis
  • Statistical Algorithms
  • Statistical Analysis
  • Stochastic Processes
  • Surveys
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • Space