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Counting algorithms for linkage.

N E Morton1, A Collins

  • 1Department of Community Medicine, Southampton General Hospital, Hants.

Annals of Human Genetics
|May 1, 1990
PubMed
Summary
This summary is machine-generated.

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The Lander-Green algorithm offers no computational advantages over the expectation-maximization (EM) algorithm for likelihood maximization. Researchers found minimal numerical differences, suggesting EM is preferable for mapping applications.

Area of Science:

  • Computational statistics
  • Geographic information systems (GIS)

Background:

  • The expectation-maximization (EM) algorithm is a standard method for likelihood maximization in statistical modeling.
  • The Lander-Green algorithm presents an alternative approach with algebraic differences.

Purpose of the Study:

  • To compare the Lander-Green algorithm with the EM algorithm for likelihood maximization.
  • To evaluate the practical utility and computational efficiency of the Lander-Green algorithm.

Main Methods:

  • Algebraic comparison of the Lander-Green and EM algorithms.
  • Empirical evaluation of numerical differences and computational performance.

Main Results:

  • The Lander-Green algorithm is algebraically distinct from the EM algorithm.

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  • Numerical differences between the two algorithms were found to be very small.
  • The Lander-Green algorithm does not offer computational ease or faster convergence compared to EM.

    • Conclusions:

    • There is no compelling reason to prefer the Lander-Green algorithm over the EM algorithm based on computational efficiency.
    • Layering techniques, as suggested by Green, can be advantageous.
    • Consideration of occasional information weight updates may accelerate mapping processes.