Weighted least squares estimation for exchangeable binary data

Dale Bowman () and E. Olusegun George ()
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Dale Bowman: The University of Memphis
E. Olusegun George: The University of Memphis

Computational Statistics, 2017, vol. 32, issue 4, No 24, 1747-1765

Abstract: 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.

Keywords: Exchangeability; Completely monotonic function; Weighted least squares; Excess risk; Benchmark dose (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00180-016-0695-x

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