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On Decomposition and Aggregation Error in Estimation: Some Basic Principles and Examples

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  • Ali Mosleh
  • Vicki Bier

Abstract

A common strategy in estimation is to decompose the quantity being estimated into several factors, estimate a value for each factor, and then reaggregate these values to obtain an overall estimate. Until now, the extent of decomposition to use has been considered largely a matter of judgment. However, both the extent of decomposition and the manner in which the results are reaggregated can have a significant effect on the resulting estimate. This paper discusses factors affecting the optimum level of decomposition, and presents examples showing that the use of nonoptimal decomposition strategies can lead to significant inaccuracies.

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  • Ali Mosleh & Vicki Bier, 1992. "On Decomposition and Aggregation Error in Estimation: Some Basic Principles and Examples," Risk Analysis, John Wiley & Sons, vol. 12(2), pages 203-214, June.
    • Handle: RePEc:wly:riskan:v:12:y:1992:i:2:p:203-214
    DOI: 10.1111/j.1539-6924.1992.tb00668.x
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    References listed on IDEAS

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    Cited by:

    1. Vicki M. Bier & Yacov Y. Haimes & James H. Lambert & Nicholas C. Matalas & Rae Zimmerman, 1999. "A Survey of Approaches for Assessing and Managing the Risk of Extremes," Risk Analysis, John Wiley & Sons, vol. 19(1), pages 83-94, February.
    2. Tzu-Tsung Wong, 2007. "Perfect aggregation of Bayesian analysis on compositional data," Statistical Papers, Springer, vol. 48(2), pages 265-282, April.

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