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Flexible parametric measurement error models.

R J Carroll1, K Roeder, L Wasserman

  • 1Department of Statistics, Texas A&M University, College Station 77843-3143, USA.

Biometrics
|April 25, 2001
PubMed
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Flexible parametric models using mixtures of normals reduce sensitivity to assumptions in measurement error models. This approach improves estimate consistency and retains efficiency for linear errors-in-variables and change-point Berkson models.

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Inferences in measurement error models are often sensitive to underlying assumptions.
  • Incorrect model specification can lead to inconsistent parameter estimates.
  • Standard parametric models may not adequately capture complex data structures.

Purpose of the Study:

  • To propose a flexible parametric modeling approach to reduce sensitivity to assumptions in measurement error models.
  • To retain the efficiency of parametric inference while accommodating departures from standard models.
  • To investigate the performance of this approach in specific model settings.

Main Methods:

  • Utilizing flexible parametric models, specifically mixtures of normal distributions.
  • Applying the proposed method to a linear errors-in-variables model.

Related Experiment Videos

  • Applying the proposed method to a change-point Berkson model.

    • Main Results:

    • The proposed flexible models accommodate departures from standard assumptions.
    • The approach helps mitigate the inconsistency of estimates caused by model misspecification.
    • Efficiency comparable to standard parametric methods is maintained.

    Conclusions:

    • Mixtures of normals offer a robust alternative for measurement error modeling.
    • This flexible approach enhances the reliability of statistical inferences.
    • The method is effective for both linear errors-in-variables and change-point Berkson models.