Data Modeling Using Quantile and Density-Quantile Functions.

Abstract

Statistical data modeling is a field of statistical reasoning that seeks to fit models to data without using models based on prior theory; rather one seeks to learn the model by a process which could be called statistical model identification. When analyzing a sample X sub 1, ..., X sub n, statisticians should not confine themselves to either fitting a Gaussian distribution, or transforming the data to be Gaussian. Such an approach ignores the importance of bimodality as a feature of observed data, and also ignores the need to fit to data probability model based distributions which could suggest probability models for the causes generating the data. This paper describes an approach to statistical data modeling which emphasizes estimation of quantile and density-quantile functions; it treats the Gaussian distribution as just one of the available distributions. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1980
Accession Number
ADA084746

Entities

People

  • Emanuel Parzen

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Data Analysis
  • Data Mining
  • Data Modeling
  • Data Science
  • Distribution Functions
  • Estimators
  • Gaussian Distributions
  • Information Science
  • Maximum Likelihood Estimation
  • Order Statistics
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Statistical Data
  • Statistics
  • Stochastic Processes

Readers

  • Statistical inference.