What’s your Hall number?

Today I attended the funeral of Peter Hall, one of the finest mathematical statisticians ever to walk the earth and easily the best from Australia. One of the most remarkable things about Peter was his astonishing productivity, with over 600 papers. As I sat in the audience I realised that many of the people there were probably coauthors of papers with Peter, and I wondered how many statisticians in the world would have been his coauthors or second-degree co-authors.

In mathematics, people calculate Erdős numbers — the “collaborative distance” between Paul Erdős and another person, as measured by authorship of mathematical papers. An Erdős number of 1 means you wrote a paper with Erdős; an Erdős number of 2 means you wrote a paper with someone who has an Erdős number of 1; and so on. My Erdős number is 3, measured in two different ways:

  • via Peter Brockwell / Kai-Lai Chung / Paul Erdös
  • via J. Keith Ord / Peter C Fishburn / Paul Erdös

It seems appropriate that we should compute Hall numbers in statistics. Mine is 1, as I was lucky enough to have coauthored two papers with Peter Hall. You can compute your own Hall number here. Just put your own surname in the second author field.



George E P Box (1919-2013)

Last Thursday (28 March 2013), George Box passed away at the age of 93. He was one of the great statisticians of the last 100 years, and leaves an astonishingly diverse legacy.

When I teach forecasting to my second year commerce students, we cover Box-Cox transformations, Box-Pierce and Ljung-Box tests, and Box-Jenkins modelling, and my students wonder if it is the same Box in all cases. It is. And we don’t even go near his work on response surface modelling, design of experiments, quality control or random number generation. Occasionally, a student wonders if boxplots are also due to GEP Box, but they were the brainchild of his good friend John W Tukey.

I often quote Box’s famous words to my students “All models are wrong but some are useful” (Box, GEP, 1979, Robustness in the strategy of scientific model building, Robustness in Statistics, Academic Press, pp.201-236.) This summarises my view of statistical modelling perfectly — no-one should believe their models; instead, treat them as tools to be used to assist in understanding the data. Continue reading →

Clive Granger (1934-2009)

Sir Clive Granger has died at the age of 74. There are some nice obituaries in the New York Times and the Daily Telegraph. Also, his Wikipedia page has some good information. I met Clive on several occasions and he was “a scholar and a gentleman”, a remarkably humble man given his outstanding achievements and someone who was always willing to help young researchers. The world of forecasting will miss him.