Weighted least squares estimation for exchangeable binary data
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.
Publication Title
Computational Statistics
Recommended Citation
Bowman, D., & George, E. (2017). Weighted least squares estimation for exchangeable binary data. Computational Statistics, 32 (4), 1747-1765. https://doi.org/10.1007/s00180-016-0695-x
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