Interval estimation via tail functions

Borek Puza*, Terence O'Neill

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

Abstract

The authors describe a new method for constructing confidence intervals. Their idea consists in specifying the cutoff points in terms of a function of the target parameter rather than as constants. When it is suitably chosen, this so-called tail function yields shorter confidence intervals in the presence of prior information. It can also be used to improve the coverage properties of approximate confidence intervals. The authors illustrate their technique by application to interval estimation of the mean of Bernoulli and normal populations. They further suggest guidelines for choosing the optimal tail function and discuss the relationship with Bayesian inference.

Original languageEnglish
Pages (from-to)299-310
Number of pages12
JournalCanadian Journal of Statistics
Volume34
Issue number2
DOIs
Publication statusPublished - 1 Jun 2006
Externally publishedYes

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