By D. R. Cox
Publisher: Cambridge University Press
Print Publication Year: 2006
Online Publication Date:March 2011
Online ISBN:9780511813559
Hardback ISBN:9780521866736
Paperback ISBN:9780521685672
Chapter DOI: http://dx.doi.org/10.1017/CBO9780511813559.011
Subjects: Statistical theory and methods, Quantitative biology, biostatistics and mathematical modeling
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A very thorough account of the history of the more mathematical side of statistics up to the 1930's is given by Hald (1990, 1998, 2006). Stigler (1990) gives a broader perspective and Heyde and Seneta (2001) have edited a series of vignettes of prominent statisticians born before 1900.
Many of the great eighteenth and early nineteenth century mathematicians had some interest in statistics, often in connection with the analysis of astronomical data. Laplace (1749–1827) made extensive use of flat priors and what was then called the method of inverse probability, now usually called Bayesian methods. Gauss (1777–1855) used both this and essentially frequentist ideas, in particular in his development of least squares methods of estimation. Flat priors were strongly criticized by the Irish algebraist Boole (1815–1864) and by later Victorian mathematicians and these criticisms were repeated by Todhunter (1865) in his influential history of probability. Karl Pearson (1857–1936) began as, among other things, an expert in the theory of elasticity, and brought Todhunter's history of that theory to posthumous publication (Todhunter, 1886, 1893).
In one sense the modern era of statistics started with Pearson's (1900) development of the chi-squared goodness of fit test. He assessed this without comment by calculating and tabulating the tail area of the distribution. Pearson had some interest in Bayesian ideas but seems to have regarded prior distributions as essentially frequency distributions.
pp. i-iv
pp. v-viii
pp. ix-xii
pp. xiii-xvi
pp. 1-16
2 - Some concepts and simple applications : Read PDF
pp. 17-29
3 - Significance tests : Read PDF
pp. 30-44
4 - More complicated situations : Read PDF
pp. 45-63
5 - Interpretations of uncertainty : Read PDF
pp. 64-95
6 - Asymptotic theory : Read PDF
pp. 96-132
7 - Further aspects of maximum likelihood : Read PDF
pp. 133-160
8 - Additional objectives : Read PDF
pp. 161-177
9 - Randomization-based analysis : Read PDF
pp. 178-193
Appendix A - A brief history : Read PDF
pp. 194-196
Appendix B - A personal view : Read PDF
pp. 197-200
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pp. 213-219