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Weighted least squares estimation for exchangeable binary data

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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.

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Authors and Affiliations

  1. Department of Mathematical Science, The University of Memphis, Memphis, TN, 38152, USA

    Dale Bowman & E. Olusegun George

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  1. Dale Bowman
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  2. E. Olusegun George
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Correspondence to Dale Bowman.

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Bowman, D., George, E.O. Weighted least squares estimation for exchangeable binary data. Comput Stat 32, 1747–1765 (2017). https://doi.org/10.1007/s00180-016-0695-x

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  • DOI: https://doi.org/10.1007/s00180-016-0695-x

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