A mathematical Examination of the Methods of determining the Accuracy of Observation by the Mean Error, and by the Mean Square Error

By R. Fisher. 1920

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Citations in the corpus

The hardness of conditional independence testing and the generalised covariance measure (2018, background): “Conditional independences lie at the heart of several fundamental concepts such as sufficiency [21] and ancillarity [22, 23]; see also Jensen and Sørensen [28].”
Theory & Methods: A Weighted Least Squares Approach to Levene's Test of Homogeneity of Variance (2000, unknown): “Following Fisher (1920), E(uij ) = √ 2 π σi , var(uij ) = ( 1 − 2 π ) σ 2i , (5) cov(uij , uik) = 2 π ( 1 ni − 1 )(√ ni(ni − 2) + sin−1 1 ni − 1 − (ni − 1) ) σ 2i (j 6= k).”
Asymptotics and the theory of inference (2003, methodology): “Fraser, Wong and Wu (1999) study a class of regression models of the form y = x(β) + σe, where x(β) is a known and possibly nonlinear function of x and unknown parameter β, and e is either a normal or t distribution. In this class of models the pivotal statistic z = {y − x(β)}/σ leads to an explicit”
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).”
The epic story of maximum likelihood (2007, unknown): “Two more years passed, and in 1918 Fisher discovered sufficiency in the context of estimating the normal standard deviation (Fisher, 1920); he recalled Pearson’s challenge to produce a rationale for the method, and he was off to the races, quickly setting to work on the monumental paper on the…”
R.A. Fisher and the making of maximum likelihood 1912-1922 (1997, background): “Fisher 1920, p. 768 ends by discussing a ‘‘qualitative distinction, which reveals the unique character of s .’’”