Quantiles, Parametric-Select Density Estimations, and Bi-Information Parameter Estimators.

Abstract

This paper outlines a quantile-based approach to functional inference problems in which the parameters to be estimated are density functions. Exponential models and autoregressive models are approximating densities which can be justified as maximum entropy for respectively the entropy of a probability density and the entropy of a quantile density. It is proposed that bi-information estimation of a density function can be developed by analogy to the problem of identification of regression models. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1982
Accession Number
ADA117460

Entities

People

  • Emanuel Parzen

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Distribution Functions
  • Information Science
  • Normal Distribution
  • Order Statistics
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Statistical Data
  • Statistical Inference
  • Statistical Samples
  • Statistics
  • Universities

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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