R, Extreme Value Statistics and Missing Data

July 16, 2015

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

by Joseph Rickert

June was a hot month for extreme statistics and R. Not only did we close out the month with useR! 2015, but two small conferences in the middle of the month brought experts together from all over the world to discuss two very difficult areas of statistics that generate quite a bit of R code.

The Extreme Value Analysis conference is a prestigious event that is held every two years in different parts of the world. This year, over 230 participants from 26 countries met from June 15th through 19th at the University of Michigan, Ann Arbor for EVA 2015. The program included theoretical advances as well as novel applications of Extreme Value Theory in fields including finance,


economics,insurance, hydrology, traffic safety, terrorism risk, climate and environmental extremes. You can get a good idea of the topics discussed at the EVA from the book of abstracts which includes an author index as well as a keyword index. The conference organizers are in the process of obtaining permissions to post the slides from the talk. These should be available soon.

In the meantime, have a look at the slides from two excellent presentations from the Workshop on Statistical Computing which was held the day before the main conference. Eric Gilleland's Introduction to Extreme Value Analysis provides a gentle introduction for anyone willing to look at some math. Eric begins with some motivating examples, develops some key concepts and illustrates them with R and even provides some history along the way. This quote from Emil Gumbel, a founding giant in the field, should be every modeler's mantra: “Il est impossible que l’improbable n’arrive jamais”. ("It's impossible for the improbable to never occur" — ed)


In Modeling spatial extremes with the SpatialExtremes package, Mathieu Ribatet works through a  complete example in R by fitting and evaluating a model and running simulations. This motivating slide from the presentation describes the kind of problems he is considering.

In our world of climate extremes and financial black swans there are probably few topics more of more immediate concern to statisticians that EVA, but the vexing problem of dealing with missing values might be one of them. So, it was not surprising that at nearly the same time (June 18th and 19th) a 150 people or so gathered on the other side of the world in Rennes France for missData 2015.

Missing_booksOver the years, R developers have expended considerable energy creating routines to missing values. The transcan function in the Hmisc package "automatically transforms continuous and categorical variables to have maximum correlation with the best linear combination of the other variables". mice provides functions using the Fully Conditional Specification using the MICE algorithm. (See the slides from Stef van Buuern's presentation Fully Conditional Specification: Past, present and beyond for a perspective on FCS and the reading list at left.) mi provides functions for missing value imputation in a Bayesian framework as does the BaBooN package and VIM provides for visualizing the structure of missing values. Slides for almost all of the talks are available online at the conference program page and videos will be available soon. Have a look at the slides from the lightning talk by Matthias Templ and Alexander Kowarik to see what the VIM package can do.


Revolution Analytics was very pleased to have been able to sponsor both of these conferences. For the next EVA mark your calendars to visit Delft, the Netherlands in 2017.

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

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