Weighted L-Squared Quantile Distance Estimators for Randomly Censored Data.

Abstract

The asymptotic properties of a family of minimum quantile function distance estimators for randomly censored data sets are considered. These procedures produce an estimator of the parameter vector that minimizes a weighted L squared distance measure between the Kaplan-Meier quantile function and an assumed parametric family of quantile functions. Regularity conditions are provided which insure that these estimators are consistent and asymptotically normal. An optimal weight function is derived for single parameter families, which, for location/scale families, results in censored sample analogs of estimators such as those suggested by Parzen (1979a, 1979b), and Weiss and Wolfowitz (1970).

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1981
Accession Number
ADA109544

Entities

People

  • R. L. Eubank
  • V. N. Lariccia

Organizations

  • Southern Methodist University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Computations
  • Data Science
  • Data Sets
  • Distribution Functions
  • Distribution Theory
  • Estimators
  • Gaussian Processes
  • Information Science
  • Models
  • Order Statistics
  • Probability
  • Probability Density Functions
  • Random Variables
  • Statistics
  • Theorems
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Statistical inference.