Home > Research > Publications & Outputs > Computation of an exact confidence set for a ma...

Electronic data

  • 1-s2.0-S0167715216302395-main

    Rights statement: This is the author’s version of a work that was accepted for publication in Statistics & Probability Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Statistics & Probability Letters, 122, 2016 DOI: 10.1016/j.spl.2016.10.032

    Accepted author manuscript, 259 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

Computation of an exact confidence set for a maximum point of a univariate polynomial function in a given interval

Research output: Contribution to journalJournal article

Published
Close
<mark>Journal publication date</mark>03/2017
<mark>Journal</mark>Statistics and Probability Letters
Volume122
Number of pages5
Pages (from-to)157-161
Publication statusPublished
Early online date18/11/16
Original languageEnglish

Abstract

Construction of a confidence set for a maximum point of a function is an important statistical problem. Wan et al., (2015) provided an exact 1−α1−α confidence set for a maximum point of a univariate polynomial function in a given interval. In this paper, we give an efficient computational method for computing the confidence set of Wan et al., (2015). We demonstrate with two examples that the new method is substantially more efficient than the proposals by Wan et al., (2015). Matlab programs have been written which make the implementation of the new method straightforward.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Statistics & Probability Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Statistics & Probability Letters, 122, 2016 DOI: 10.1016/j.spl.2016.10.032