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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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    1. Anthony Y. C. Kuk, 2004. "A litter‐based approach to risk assessment in developmental toxicity studies via a power family of completely monotone functions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 369-386, April.
    2. Chang Yu & Daniel Zelterman, 2002. "Statistical Inference for Familial Disease Clusters," Biometrics, The International Biometric Society, vol. 58(3), pages 481-491, September.
    3. Bowman, Dale, 1999. "A parametric independence test for clustered binary data," Statistics & Probability Letters, Elsevier, vol. 41(1), pages 1-7, January.
    4. Anestis Touloumis & Alan Agresti & Maria Kateri, 2013. "GEE for Multinomial Responses Using a Local Odds Ratios Parameterization," Biometrics, The International Biometric Society, vol. 69(3), pages 633-640, September.
    5. Yu, Chang & Zelterman, Daniel, 2008. "Sums of exchangeable Bernoulli random variables for family and litter frequency data," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1636-1649, January.
    6. N. R. Parsons & R. N. Edmondson & S. G. Gilmour, 2006. "A generalized estimating equation method for fitting autocorrelated ordinal score data with an application in horticultural research," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(4), pages 507-524, August.
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