Generalized bootstrap for estimators of minimizers of convex functions

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

We introduce a generalized bootstrap technique for estimators obtained by minimizing functions that are convex in the parameter. We establish the consistency of these schemes via representation theorems. A number of classical resampling schemes, like the delete-d jackknife may be treated as special cases of this generalized bootstrap; and new ways of resampling are also introduced. Some of the schemes are computationally more efficient than classical techniques.

Original languageEnglish (US)
Pages (from-to)225-239
Number of pages15
JournalJournal of Statistical Planning and Inference
Volume117
Issue number2
DOIs
StatePublished - Dec 1 2003

Fingerprint

Minimizer
Bootstrap
Convex function
Resampling
Estimator
Jackknife
Representation Theorem
Representation theorem

Keywords

  • Bootstrap
  • Jackknife
  • L median
  • M estimators
  • Oja median
  • U-statistics

Cite this

Generalized bootstrap for estimators of minimizers of convex functions. / Bose, Arup; Chatterjee, Snigdhansu.

In: Journal of Statistical Planning and Inference, Vol. 117, No. 2, 01.12.2003, p. 225-239.

Research output: Contribution to journalArticle

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