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Quantitative Analysis of Variability and Uncertainty with Known Measurement Error: Methodology and Case Study

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  • Junyu Zheng
  • H. Christopher Frey

Abstract

The appearance of measurement error in exposure and risk factor data potentially affects any inferences regarding variability and uncertainty because the distribution representing the observed data set deviates from the distribution that represents an error‐free data set. A methodology for improving the characterization of variability and uncertainty with known measurement errors in data is demonstrated in this article based on an observed data set, known measurement error, and a measurement‐error model. A practical method for constructing an error‐free data set is presented and a numerical method based upon bootstrap pairs, incorporating two‐dimensional Monte Carlo simulation, is introduced to address uncertainty arising from measurement error in selected statistics. When measurement error is a large source of uncertainty, substantial differences between the distribution representing variability of the observed data set and the distribution representing variability of the error‐free data set will occur. Furthermore, the shape and range of the probability bands for uncertainty differ between the observed and error‐free data set. Failure to separately characterize contributions from random sampling error and measurement error will lead to bias in the variability and uncertainty estimates. However, a key finding is that total uncertainty in mean can be properly quantified even if measurement and random sampling errors cannot be separated. An empirical case study is used to illustrate the application of the methodology.

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  • Junyu Zheng & H. Christopher Frey, 2005. "Quantitative Analysis of Variability and Uncertainty with Known Measurement Error: Methodology and Case Study," Risk Analysis, John Wiley & Sons, vol. 25(3), pages 663-675, June.
    • Handle: RePEc:wly:riskan:v:25:y:2005:i:3:p:663-675
    DOI: 10.1111/j.1539-6924.2005.00620.x
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    References listed on IDEAS

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    1. Yuchao Zhao & H. Christopher Frey, 2004. "Quantification of Variability and Uncertainty for Censored Data Sets and Application to Air Toxic Emission Factors," Risk Analysis, John Wiley & Sons, vol. 24(4), pages 1019-1034, August.
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    5. Junyu Zheng & H. Christopher Frey, 2004. "Quantification of Variability and Uncertainty Using Mixture Distributions: Evaluation of Sample Size, Mixing Weights, and Separation Between Components," Risk Analysis, John Wiley & Sons, vol. 24(3), pages 553-571, June.
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    2. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
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    4. John P. Buonaccorsi & Giovanni Romeo & Magne Thoresen, 2018. "Model†based bootstrapping when correcting for measurement error with application to logistic regression," Biometrics, The International Biometric Society, vol. 74(1), pages 135-144, March.

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