Selecting stopping rules for confidence interval procedures
Authors
Singham, Dashi I.
Subjects
Confidence interval procedures
Sequential stopping rules
Finite-sample performance
Coverage profiles
Sequential stopping rules
Finite-sample performance
Coverage profiles
Advisors
Date of Issue
2014
Date
Publisher
Association for Computing Machinery (ACM)
Language
Abstract
The sample size decision is crucial to the success of any sampling experiment. More samples imply better
confidence and precision in the results, but require higher costs in terms of time, computing power, and
money. Analysts often choose sequential stopping rules on an ad hoc basis to obtain confidence intervals
with desired properties without requiring large sample sizes. However, the choice of stopping rule can affect
the quality of the interval produced in terms of the coverage, precision, and replication cost. This article
introduces methods for choosing and evaluating stopping rules for confidence interval procedures.We develop
a general framework for assessing the quality of a broad class of stopping rules applied to independent and
identically distributed data. We introduce coverage profiles that plot the coverage according to the stopping
time and reveal situations when the coverage could be unexpectedly low. Finally, we recommend simple
techniques for obtaining acceptable or optimal rules.
Type
Article
Description
The article of record as published may be located at http://dx.doi.org/10.1145/2627734
Series/Report No
Department
Operations Research (OR)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
18 p.
Citation
D.I. Singham, 'Selecting stopping rules for confidence interval procedures," ACM Transactions on Modeling and Computer Simulation, Vol. 24, No. 3, Article 18, (July 2014), 18 p.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.