Archive for Kaplan-Meier estimator

[not] reading classics (#7)

Posted in Books, Kids, Statistics, University life with tags , , , , on December 15, 2013 by xi'an

La Défense from Paris-Dauphine, Nov. 15, 2012This week, I decided not to report on the paper read at the Reading Classics student seminar, as it did not work out well-enough. The paper was the “Regression models and life-table” published in 1972 by David Cox… A classic if any! Indeed, I do not think posting a severe criticism of the presentation or the presentation itself would be of much use to anyone. It is rather sad as (a) the student clearly put some effort in the presentation, including a reproduction of an R execution, and (b) this was an entry on semi-parametrics, Kaplan-Meyer, truncated longitudinal data, and more, that could have benefited the class immensely. Alas, the talk did not take any distance from the paper, did not exploit the following discussion, and exceeded by far the allocated time, without delivering a comprehensible message. It is a complex paper with concise explanations, granted, but there were ways to find easier introductions to its contents in the more recent literature… It is possible that a second student takes over and presents her analysis of the paper next January. Unless she got so scared with this presentation that she will switch to another paper… [Season wishes to Classics Readers!]

NYT obituary & reviews

Posted in Books, Statistics, University life with tags , , , , , on August 15, 2011 by xi'an

I was taking advantage of being in the US a Sunday to read the Sunday edition of the NYT yesterday and I came upon the obituary for Paul Meier, “the” Meier in the Kaplan-Meier estimator. (The NYT  actually notes that the corresponding JASA paper has been quoted more than 35,000 times!) I also learned from the NYT that Paul Meier was one of the first proponents of randomization in medical trials.

There was also an interesting review of George Martin’s  A Dance with Dragons that is waiting for me at home! Sailing the Bahamas last week means I missed the review of Sharon McGrayne’s the theory that would not die, written by John Paulos. Here is his concluding paragraph (I won’t comment since I have not read Sharon’s book yet!):

Statistics is an imperialist discipline that can be applied to almost any area of science or life, and this litany of applications is intended to be the unifying thread that sews the book into a coherent whole. It does so, but at the cost of giving it a list-like, formulaic feel. More successful are McGrayne’s vivifying sketches of the statisticians who devoted themselves to Bayesian polemics and counterpolemics. As McGrayne amply shows, orthodox Bayesians have long been opposed, sometimes vehemently, by so-called frequentists, who have objected to their tolerance for subjectivity. The nub of the differences between them is that for Bayesians the prior can be a subjective expression of the degree of belief in a hypothesis, even one about a unique event or one that has as yet never occurred. For frequentists the prior must have a more objective foundation; ideally that is the relative frequency of events in repeatable, well-defined experiments. McGrayne’s statisticians exhibit many differences, and she cites the quip that you can nevertheless always tell them apart by their posteriors, a good word on which to end.