A Density-Quantile Function Perspective on Robust Estimation.

Abstract

This paper provides an overview to a new general approach to statistical data analysis and parameter estimation which could be called the quantile function approach. The aims of descriptive statistics (to graphically summarize and display the data) are obtained by Quantile-Box plots of the sample quantile function. The aims of goodness of fit are obtained by fitting smooth quantile functions to the sample quantile function. The aims of parameter estimation, especially robust estimation of location and scale parameters, are attained by regression analysis of the sample quantile function. (The goal of a statistician in analyzing a batch of data X1,...,Xn should be both estimation of parameters and goodness of fit. By 'goodness of fit' is meant fitting of the observed sample probabilities by a smooth probability law.)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1978
Accession Number
ADA056707

Entities

People

  • Emanuel Parzen

Organizations

  • University at Buffalo

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Data Analysis
  • Data Mining
  • Data Science
  • Distribution Functions
  • Estimators
  • Information Science
  • Maximum Likelihood Estimation
  • Network Science
  • New York
  • Order Statistics
  • Probability
  • Random Variables
  • Regression Analysis
  • Statistical Algorithms
  • Statistical Data
  • Statistical Inference
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Regression Analysis.