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Related Experiment Videos

Standard errors for EM estimates in generalized linear models with random effects.

H Friedl1, G Kauermann

  • 1Institute of Statistics, Technical University Graz, Austria. friedl@stat.tu-graz.ac.at

Biometrics
|September 14, 2000
PubMed
Summary
This summary is machine-generated.

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This study presents a new method for calculating standard errors in generalized linear models with random effects using the EM algorithm. The approach utilizes quadrature formulas for accurate estimation, enabling reliable statistical inference.

Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Modeling

Background:

  • Generalized linear models (GLMs) with random effects are widely used in various scientific fields.
  • Accurate computation of standard errors for parameter estimates is crucial for statistical inference.
  • The Expectation-Maximization (EM) algorithm is a common method for estimating parameters in such models, but computing standard errors can be challenging.

Purpose of the Study:

  • To develop a procedure for computing standard errors of EM estimates in generalized linear models with random effects.
  • To address the computational challenges associated with standard error calculation in complex statistical models.
  • To provide a framework for robust inferential arguments based on EM estimates.

Main Methods:

  • Derivation of a procedure for computing standard errors of EM estimates.

Related Experiment Videos

  • Application of quadrature formulas (Gauss-Hermite quadrature and nonparametric maximum likelihood estimation) to approximate integrals within the EM algorithm.
  • Derivation of an approximation for the expected Fisher information matrix using an expansion of EM estimating equations.

    • Main Results:

    • A novel procedure for calculating standard errors of EM estimates in generalized linear models with random effects has been successfully derived.
    • The proposed method effectively handles both Gaussian and unspecified random effect distributions.
    • Simulations and an example demonstrate the validity and applicability of the derived procedure.

    Conclusions:

    • The developed procedure provides a reliable method for computing standard errors in generalized linear models with random effects estimated via the EM algorithm.
    • This work facilitates more accurate statistical inference and hypothesis testing in models with random effects.
    • The approach offers a valuable tool for researchers dealing with complex longitudinal or clustered data.