Journal of Statistical Theory and Applications

Volume 14, Issue 2, June 2015, Pages 192 - 203

Estimating Per Capita Rates Using Aggregate Measurements From Groups of Diverse Compositions

Authors
Donald N. Stengel, Priscilla Chaffe-Stengel
Corresponding Author
Donald N. Stengel
Received 16 November 2014, Accepted 25 January 2015, Available Online 30 June 2015.
DOI
10.2991/jsta.2015.14.2.7How to use a DOI?
Keywords
Estimation, Sampling, Grouped data
Abstract

This paper considers the problem of estimating a variable mean for a population of elements where data are only available as aggregate sums for groups of multiple elements. The proposed model addresses an additional complication created when the group measure includes the contribution of diverse elements that were only partially in operation or present as part of the group during the measurement period. The model also accounts for statistical dependency between the contributions of individuals belonging to the same group. The degree of statistical dependency is reflected in a correlation coefficient parameter, which, while not observable, can be adjusted to reduce heteroscedasticity in the group data. A simple example is provided to illustrate the model.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Statistical Theory and Applications
Volume-Issue
14 - 2
Pages
192 - 203
Publication Date
2015/06/30
ISSN (Online)
2214-1766
ISSN (Print)
1538-7887
DOI
10.2991/jsta.2015.14.2.7How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Donald N. Stengel
AU  - Priscilla Chaffe-Stengel
PY  - 2015
DA  - 2015/06/30
TI  - Estimating Per Capita Rates Using Aggregate Measurements From Groups of Diverse Compositions
JO  - Journal of Statistical Theory and Applications
SP  - 192
EP  - 203
VL  - 14
IS  - 2
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2015.14.2.7
DO  - 10.2991/jsta.2015.14.2.7
ID  - Stengel2015
ER  -