Counting by weighing: know your numbers with confidence
Counting by weighing: know your numbers with confidence
Counting by weighing is often more efficient than counting manually which is time consuming and prone to human errors, especially when the number of items (e.g. plant seeds, printed labels, coins) is large. The published papers in the statistical literature have focused on how to count, by weighing, a random number of items that is close to a pre-specified number in some sense. This paper considers the new problem, arising from a consultation with a company, of making inference about the number of one-penny coins in a bag with known weight for infinitely many bags,by using the estimated distribution of coin weight from one calibration data set only. Specifically, a lower confidence bound has been constructed on the number of one-penny coins for each of infinitely many future bags of one-penny coins, as required by the company. As the same calibration data set is used repeatedly in the construction of all these lower confidence bounds, the interpretation of coverage frequency of the lower confidence bounds proposed in this paper is different from that of a usual confidence set.
641-648
Liu, Wei
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Han, Yang
f5a6d423-6a9c-487c-be8d-17dcdc35829f
Bretz, Frank
aa8a675f-f53f-4c50-8931-8e9b7febd9f0
Wan, Fang
2d1357dd-b94d-418f-a721-e0e95909e39c
Yang, Ping
3dba2634-eb2b-4df8-a995-1061b841afe1
August 2016
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Han, Yang
f5a6d423-6a9c-487c-be8d-17dcdc35829f
Bretz, Frank
aa8a675f-f53f-4c50-8931-8e9b7febd9f0
Wan, Fang
2d1357dd-b94d-418f-a721-e0e95909e39c
Yang, Ping
3dba2634-eb2b-4df8-a995-1061b841afe1
Liu, Wei, Han, Yang, Bretz, Frank, Wan, Fang and Yang, Ping
(2016)
Counting by weighing: know your numbers with confidence.
Journal of the Royal Statistical Society. Series C: Applied Statistics, 65 (4), .
(doi:10.1111/rssc.12142).
Abstract
Counting by weighing is often more efficient than counting manually which is time consuming and prone to human errors, especially when the number of items (e.g. plant seeds, printed labels, coins) is large. The published papers in the statistical literature have focused on how to count, by weighing, a random number of items that is close to a pre-specified number in some sense. This paper considers the new problem, arising from a consultation with a company, of making inference about the number of one-penny coins in a bag with known weight for infinitely many bags,by using the estimated distribution of coin weight from one calibration data set only. Specifically, a lower confidence bound has been constructed on the number of one-penny coins for each of infinitely many future bags of one-penny coins, as required by the company. As the same calibration data set is used repeatedly in the construction of all these lower confidence bounds, the interpretation of coverage frequency of the lower confidence bounds proposed in this paper is different from that of a usual confidence set.
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Accepted/In Press date: 10 December 2015
e-pub ahead of print date: 1 March 2016
Published date: August 2016
Organisations:
Statistical Sciences Research Institute
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Local EPrints ID: 385482
URI: http://eprints.soton.ac.uk/id/eprint/385482
ISSN: 0035-9254
PURE UUID: 0aa51e57-732a-45c3-9003-0d30d1ac6897
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Date deposited: 20 Jan 2016 13:22
Last modified: 15 Mar 2024 02:43
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Contributors
Author:
Yang Han
Author:
Frank Bretz
Author:
Fang Wan
Author:
Ping Yang
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