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An expectation–maximization scheme for measurement error models

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  • Bhadra, Anindya

Abstract

We treat the problem of maximum likelihood estimation in measurement error models. Direct maximization of the analytically intractable likelihood in such models is difficult. We derive an efficient expectation–maximization scheme for truncated polynomial spline models of degree one. Simulation results confirm the effectiveness of the proposed method.

Suggested Citation

  • Bhadra, Anindya, 2017. "An expectation–maximization scheme for measurement error models," Statistics & Probability Letters, Elsevier, vol. 120(C), pages 61-68.
    • Handle: RePEc:eee:stapro:v:120:y:2017:i:c:p:61-68
    DOI: 10.1016/j.spl.2016.09.007
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    References listed on IDEAS

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    1. Berry S. M. & Carroll R. J & Ruppert D., 2002. "Bayesian Smoothing and Regression Splines for Measurement Error Problems," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 160-169, March.
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