A Regression Design Approach to Optimal and Robust Spacing Selection.

Abstract

The problem of location and/or scale parameter estimation using the asymptotically best linear unbiased estimator based on sample quantiles is considered. The problem of optimal spacing selection for these estimators is shown to be equivalent to the problem of regression design for time series with Brownian motion or Brownian bridge covariance structures and a particular variable knot spline approximation problem. This equivalence is employed, in conjunction with a regression framework, to investigate the asymptotic properties of certain spacing selection schemes. In particular, an asymptotic alternative is developed to a robust estimation procedure suggested by Chan ad Rhodin (1980). (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1981
Accession Number
ADA104728

Entities

People

  • Randall L. Eubank

Organizations

  • Southern Methodist University

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Brownian Motion
  • Covariance
  • Data Science
  • Equations
  • Estimators
  • Information Science
  • Mathematics
  • Order Statistics
  • Regression Analysis
  • Security
  • Sequences
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Theorems
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • Space