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Doubly misspecified models

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  • N. X. Lin
  • J. Q. Shi
  • R. Henderson

Abstract

Estimation bias arising from local model uncertainty and incomplete data has been studied by Copas & Eguchi (2005) under the assumption of a correctly specified marginal model. We extend the approach to allow additional local uncertainty in the assumed marginal model, arguing that this is almost unavoidable for nonlinear problems. We present a general bias analysis and sensitivity procedure for such doubly misspecified models and illustrate the breadth of application through three examples: logistic regression with a missing confounder, measurement error for binary responses and survival analysis with frailty. We show that a double-the-variance rule is not conservative under double misspecification. The ideas are brought together in a meta-analysis of studies of rehabilitation rates for juvenile offenders. Copyright 2012, Oxford University Press.

Suggested Citation

  • N. X. Lin & J. Q. Shi & R. Henderson, 2012. "Doubly misspecified models," Biometrika, Biometrika Trust, vol. 99(2), pages 285-298.
    • Handle: RePEc:oup:biomet:v:99:y:2012:i:2:p:285-298
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    File URL: http://hdl.handle.net/10.1093/biomet/asr085
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    1. repec:ipg:wpaper:2014-485 is not listed on IDEAS
    2. Muhammad Shahbaz & Ijaz Ur Rehman & Ahmed Taneem Muzaffar, 2015. "Re-Visiting Financial Development and Economic Growth Nexus: The Role of Capitalization in Bangladesh," South African Journal of Economics, Economic Society of South Africa, vol. 83(3), pages 452-471, September.

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