R.A. Fisher and the making of maximum likelihood 1912-1922

By John C. Aldrich. 1997

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When did Bayesian inference become "Bayesian"? (2006, background): “For example, as a student he might have been led to read papers and books dealing with such alternatives by his teacher, F.J.M. Stratton, but he rarely cited relevant precursors or alternatives to his own work (see Aldrich’s (1) account of what Fisher studied).”
Remote sensing for wetland classification: a comprehensive review (2018, methodology): “There are many classification algorithms, most commonly used of which are K nearest neighbors (KNN, Fix and Hodges 1951), Maximum Likelihood (ML, Aldrich 1997), support vector machine (SVM, Cortes and Vapnik 1995), decision tree (DT, Breiman 2001), and random forest (RF, Breiman 2001).”
Conditioning, Likelihood, and Coherence: A Review of Some Foundational Concepts (2000, background): “(See Aldrich 1997, Edwards 1997, and Fienberg 1997, for discussions on the history of likelihood.)”
Estimation Methods for Nonhomogeneous Regression Models: Minimum Continuous Ranked Probability Score versus Maximum Likelihood (2018, methodology): “In the field of statistics, regression coefficients and distribution parameters have traditionally mostly been estimated with maximum likelihood estimation (Aldrich 1997; Stigler 2007).”
The MM, ME, ML, EL, EF and GMM approaches to estimation: a synthesis (2002, background): “Aldrich (1997), Edwards (1997b) and Hald (1999) examined Fisher’s paradoxical views to “inverse probability” in detail.”
The Reverend Thomas Bayes FRS: a Biography to Celebrate the Tercentenary of his Birth (2004, background): “(This story has often been told in recent years; see Aldrich, 1997, and Edwards, 1997, and references therein.)”
But you have to remember P.J. Daniell of Sheffield (2007, background): “While Fisher (1922) can be interpreted as the last stage in a struggle to escape from “inverse probability” and from what he had been taught as an undergraduate–see Aldrich (1997, 2006a)–no such ghosts are lurking in Daniell (1920c).”
REVISITING A 90-YEAR-OLD DEBATE: THE ADVANTAGES OF THE MEAN DEVIATION (2005, background): “Fisher (1920) countered Eddington’s empirical evidence with a mathematical argument that SD was more efficient than MD under ideal circumstances , and many commentators now accept that Fisher provided a complete defence of the use of SD (e.g. Aldrich, 1997; MacKenzie, 1981).”