Stephen M. Stigler

Read this mostly b/c I like to learn about the original problems people were trying to solve when they came up with tools we use today, and what was the state of the art before that. I liked the choice of topics for each chapter (each of them does talk about something different and I have a better global view of the field than when I started). Highlights for me are the chapter on likelihood, the one on experiment design and the one on residuals.

I disliked the writing style: the interesting (eg. along the lines of Laplace was trying to do X, he drew this Figure) is intertwined with the pedantic (eg. He won a prize, he corresponded with X, he had an enmity with Y, he was a member of such and such society) at such small granularity that forces you to read it... I put it down for several weeks a couple of times.… (plus d'informations)

Why Seven? Stephen Stigler notes that the title of his Seven Pillars of Statistical Wisdom is borrowed from T.E. Lawrence's own Seven Pillars of Wisdom, and that both he and Lawrence of Arabia drew on Proverbs 9:1 as a source: "Wisdom hath built her house, she hath hewn out her seven pillars" (3). With this bow to tradition, Stigler goes on to note that an eighth pillar may well be forthcoming, without commenting on what it might be (203).

While we await this possible eighth pillar, the seven current pillars are: Aggregation, Information, Likelihood, Intercomparison, Regression, Design, and Residual. While the delineation is subjective, Stigler shows a strong grasp of the material by tracing the history of each of these "bins." He finds interesting things to state about each pillar (Aggregation "inherently involves the discarding of information, an act of 'creative destruction'", 196), but lacks fuller development.

There is a strong References section, but for a book published by an academic press, the footnotes are a little light (the 33 notes in the fifth chapter, "Regression," are an outlier). While the book is not dryly "academic," neither is it an introductory text; the reader needs a general understanding of numerous statistical concepts in order to grasp the subjectivity of Stigler's slicing. It thus occupies a sort of middle ground: too short and subjective for the specialist, yet a bit too specialized and obtuse for the lay reader. I would very much like to see a fuller treatment.… (plus d'informations)