ArticleThe American statistician2025
A Cornucopia of Maximum Likelihood Algorithms.
Article in The American statistician, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Generalizations of the quadratic bound optimization principle.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
Classroom expositions of maximum likelihood estimation (MLE) rely on traditional calculus methods to construct analytic solutions. This creates in students a false sense of the ease with which MLE problems can be attacked. In a nod to reality, some teachers mention and apply Newton's method, Fisher scoring, and the expectation-maximization (EM) algorithm. Although preferable to leaving students in a state of ignorance, such brief expositions ultimately fail to expose the full body of relevant techniques. Some of these techniques extend more readily to high-dimensional data problems than Newton's method and scoring. The current paper emphasizes block ascent and descent, profile likelihoods, the minorization-maximization (MM) principle, and their creative combination. These themes are put to work in readable Julia code to solve several MLE problems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.