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Accounting for measurement error in log regression models with applications to accelerated testing

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  • Robert Richardson
  • H Dennis Tolley
  • William E Evenson
  • Barry M Lunt

Abstract

In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

Suggested Citation

  • Robert Richardson & H Dennis Tolley & William E Evenson & Barry M Lunt, 2018. "Accounting for measurement error in log regression models with applications to accelerated testing," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-13, May.
    • Handle: RePEc:plo:pone00:0197222
    DOI: 10.1371/journal.pone.0197222
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    References listed on IDEAS

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    1. Laurence S. Freedman & Vitaly Fainberg & Victor Kipnis & Douglas Midthune & Raymond J. Carroll, 2004. "A New Method for Dealing with Measurement Error in Explanatory Variables of Regression Models," Biometrics, The International Biometric Society, vol. 60(1), pages 172-181, March.
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