FUN.STAT Quantile Approach to Two Sample Statistical Data Analysis.

Abstract

FUN.STAT is a name proposed to describe a synthesis of statistical reasoning which combines quantiles and quantile-densities, information and entropy, and functional statistical inference. This paper describes a FUN.STAT approach to the problem of statistical data analysis of two random samples, respectively representing two populations of interest. It is composed of four parts. Part 1 describes how conventional approaches to two sample problems, including representations of linear rank statistics, are equivalent to functionals of a stochastic process. Part 2 motivates the autoregressive density estimation approach to the problem of functional statistical inference of this stochastic process and states several conjectures concerning the properties of the density estimation approach. Part 3 outlines heuristic derivations of the asymptotic distribution theory of the process. Part 4 provides a summary and an example, using TWOSAM which is a computer program for autoregressive two sample statistical data analysis; it has been implemented as a Fortran program and as a SAS procedure.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1983
Accession Number
ADA127672

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
  • Distribution Functions
  • Estimators
  • Information Science
  • Network Science
  • New York
  • Optimal Estimators
  • Order Statistics
  • Probability
  • Random Variables
  • Regression Analysis
  • Statistical Algorithms
  • Statistical Data
  • Statistical Inference
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Artificial Intelligence
  • Statistical inference.

Technology Areas

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