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Weighted least squares estimation for exchangeable binary data

Author

Listed:
  • Dale Bowman

    (The University of Memphis)

  • E. Olusegun George

    (The University of Memphis)

Abstract

Parametric models of discrete data with exchangeable dependence structure present substantial computational challenges for maximum likelihood estimation. Coordinate descent algorithms such as the Newton’s method are usually unstable, becoming a hit or miss adventure on initialization with a good starting value. We propose a method for computing maximum likelihood estimates of parametric models for finitely exchangeable binary data, formalized as an iterative weighted least squares algorithm.

Suggested Citation

  • Dale Bowman & E. Olusegun George, 2017. "Weighted least squares estimation for exchangeable binary data," Computational Statistics, Springer, vol. 32(4), pages 1747-1765, December.
    • Handle: RePEc:spr:compst:v:32:y:2017:i:4:d:10.1007_s00180-016-0695-x
    DOI: 10.1007/s00180-016-0695-x
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    References listed on IDEAS

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