A history of parametric statistical inference from Bernoulli to Fisher, 1713-1935 /
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| Format: | eBook |
| Language: | English |
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New York :
Springer,
[2007]
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| Series: | Sources and studies in the history of mathematics and physical sciences.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book Publisher description |
Table of Contents:
- pt. 3. The central limit theorem and linear minimum variance estimation by Laplace and Gauss
- 12. Laplace's central limit theorem and linear minimum variance estimation
- 13. Gauss's theory of linear minimum variance estimation
- pt. 4. Error theory, skew distributions : Correlation, sampling distributions
- 14. The development of a frequentist error theory
- 15. Skew distributions and the method of moments
- 16. Normal correlation and regression
- 17. Sampling distributions under normality, 1876-1908
- pt. 5. The Fisherian revolution, 1912-1935
- 18. Fisher's early papers, 1912-1921
- 19. The revolutionary paper, 1922
- 20. Studentization, the F distribution, and the analysis of variance, 1922-1925
- 21. The likelihood function, ancillarity, and conditional inference
- References
- Subject index
- Author index.
- Preface
- 1. The three revolutions in parametric statistical inference
- pt. 1. Binomial statistical inference : the three pioneers : Bernoulli (1713), de Moivre (1733), and Bayes (1764)
- 2. James Bernoulli's law of large numbers for the binomial, 1713, and its generalization
- 3. De Moivre's normal approximation to the binomial, 1733, and its generalization
- 4. Bayes's posterior distribution of the binomial parameter and his rule for inductive inference, 1764
- pt. 2. Statistical inference by inverse probability : Inverse probability from Laplace (1774), and Gauss (1809) to Edgeworth (1909)
- 5. Laplace's theory of inverse probability, 1774-1786
- 6. A nonprobabilistic interlude: the fitting of equations to data, 1750-1805
- 7. Gauss's derivation of the normal distribution and the method of least squares, 1809
- 8. Credibility and confidence intervals by Laplace and Gauss
- 9. The multivariate posterior distribution
- 10. Edgeworth's genuine inverse method and the equivalence of inverse and direct probability in large samples, 1908 and 1909
- 11. Criticisms of inverse probability