Confidence limits to the distance of the true distribution from a misspecified family by bootstrap

G. Jogesh Babu, C. R. Rao

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

In statistical practice, an estimated distribution function (d.f.) from a specified family is used for taking decisions. When the true d.f. from which samples are drawn does not belong to the specified family, it is of interest to know how close the true d.f. is to the specified family. In this paper, we use non-parametric bootstrap to obtain confidence limits to the difference between the true d.f. and a member of the specified family closest to it in the sense of Kullback-Leibler measure.

Original languageEnglish (US)
Pages (from-to)471-478
Number of pages8
JournalJournal of Statistical Planning and Inference
Volume115
Issue number2
DOIs
StatePublished - Aug 1 2003

All Science Journal Classification (ASJC) codes

  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Statistics and Probability

Fingerprint Dive into the research topics of 'Confidence limits to the distance of the true distribution from a misspecified family by bootstrap'. Together they form a unique fingerprint.

  • Cite this