Improvement of Kernel Estimators of the Failure Rate Function Using the Generalized Jacknife.

Abstract

In this paper we explore methods by which the rate of convergence of the bias and the mean square error of kernel estimators of the failure rate function can be improved. We show that if the kernel is not restricted to be nonnegative, and is suitably chosen, then the bias contribution to the asymptotic mean square error can be eliminated to any required order, and the rate of convergence of the asymptotic mean square error can be brought as close to 1/n as is desired. The generalized jackknife method of combining estimators is shown to be an adequate procedure which leads us to this goal. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 14, 1980
Accession Number
ADA092928

Entities

People

  • Man-yuen Wong
  • Nozer Singpurwalla

Organizations

  • George Washington University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Convergence
  • Data Science
  • Discrete Distribution
  • Distribution Functions
  • Engineering
  • Estimators
  • Experimental Design
  • Information Science
  • Military Research
  • Numbers
  • Probability
  • Probability Density Functions
  • Random Variables
  • Real Numbers
  • Sequences
  • Statistical Samples
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.