Estimating Per Capita Rates Using Aggregate Measurements From Groups of Diverse Compositions
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 ele...
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| Published in | Journal of statistical theory and applications Vol. 14; no. 2; pp. 192 - 203 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.06.2015
Springer Nature B.V Springer |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1538-7887 2214-1766 2214-1766 1538-7887 |
| DOI | 10.2991/jsta.2015.14.2.7 |
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| 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. |
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| AbstractList | 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. |
| Author | Chaffe-Stengel, Priscilla Stengel, Donald N. |
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| DOI | 10.2991/jsta.2015.14.2.7 |
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| References | IBM Corporation, IBM SPSS Algorithms, t Test Algorithms, One Sample t Test, [online]. Available at: http://129.8.241.71:56773/help/index.jsp?topic=%2Fcom.ibm.spss.statistics.algorithms%2Falg_introduction.htm CochranWGSampling Techniques19773New York, NYJohn Wiley & Sons BhattiMCluster Effects in Mining Complex Data2012New York, NYNova Science Publisher’s BreuschTSPaganARA Simple Test for Heteroscedasticity and Random Coefficient VariationEconometrica197947512871294 FellerWAn Introduction to Probability Theory and its Applications19673New York, NYJohn Wiley & Sons WhiteHA Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for HeteroskedasticityEconometrica1980484817838 ScottAJHoltDThe Effect of Two-Stage Sampling on Ordinary Least Squares MethodsJournal of the American Statistical Association198277380848854 SAS Institute, Inc., SAS/STAT 9.2 User’s Guide, Chapter 92 (SAS Institute, Cary, NC, 2009). Levene, H., Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, eds. Olkin, Hotelling, et al (Stanford, CA: Stanford University Press, Stanford, CA, 1960), pp. 278–292. |
| References_xml | – reference: SAS Institute, Inc., SAS/STAT 9.2 User’s Guide, Chapter 92 (SAS Institute, Cary, NC, 2009). – reference: BhattiMCluster Effects in Mining Complex Data2012New York, NYNova Science Publisher’s – reference: ScottAJHoltDThe Effect of Two-Stage Sampling on Ordinary Least Squares MethodsJournal of the American Statistical Association198277380848854 – reference: FellerWAn Introduction to Probability Theory and its Applications19673New York, NYJohn Wiley & Sons – reference: IBM Corporation, IBM SPSS Algorithms, t Test Algorithms, One Sample t Test, [online]. Available at: http://129.8.241.71:56773/help/index.jsp?topic=%2Fcom.ibm.spss.statistics.algorithms%2Falg_introduction.htm – reference: WhiteHA Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for HeteroskedasticityEconometrica1980484817838 – reference: CochranWGSampling Techniques19773New York, NYJohn Wiley & Sons – reference: BreuschTSPaganARA Simple Test for Heteroscedasticity and Random Coefficient VariationEconometrica197947512871294 – reference: Levene, H., Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, eds. Olkin, Hotelling, et al (Stanford, CA: Stanford University Press, Stanford, CA, 1960), pp. 278–292. |
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| Title | Estimating Per Capita Rates Using Aggregate Measurements From Groups of Diverse Compositions |
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