Statistics and science

That’s a very broad brief, set by the London Workshop on the future of the statistical sciences (which is equally broad). There’s a great set of seven modern areas where statistics is making a contribution, which should capture the attention of the public straight away. Then there’s a collection of big hairy problems – anyone keen to tackle big data, climate change, reproducibility of research? But the idea that I will try to use in my interactions with colleagues into the future is that statistics is not well descrbibed by the phrase “the science of data”, but much better by the phrase “the science of uncertainty.” That’s something that many scientists don’t necessarily have, the understanding of uncertainty, and that’s what I want to instil in my students.

You can find the report at www.worldofstatistics.org.

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OLT information session

About 30 academics, PhD students and academic support staff gathered at ANU on 20 October to hear the words “from the horse’s mouth” about OLT grants for 2015. I do wonder about how willing an institution would be to let go of a successful program that might otherwise be thought of as a point of difference. But that aside, the session on impact was really valuable and I came away with a couple of ideas bouncing around in my head.

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Royal Society Vision for Science and Mathematics Education

Well the Brits know what they want and they have a few ideas as to how to get it! This is an inspiring document setting out where the UK wants to be in 2030 in terms of STEM education. It’ll be interesting to see how they get on in the next decades.
www.royalsociety.org/vision

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Ada Lovelace Day

Today, 14 October, is Ada Lovelace Day. We celebrate the contribution of Ada and women down the ages to science, technology, engineering and mathematics (STEM). Ada Lovelace is celebrated as the world’s first computer programmer, who saw the potential of Charles Babbage’s Difference Engine and Analytical Engine. I hope that in years to come to organise events to encourage girls to participate in STEM in their school and University studies.

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Influence diagnostics in the elliptical linear model with restrictions

Shuangzhe Liu from the Maths & Stats Academic program gave this talk on 3 October, a longer version of the presentation he gave at ASC in Sydney in July. He used both simulations and published data sets to illustrate the use of local influence and Cooks distance. The exact restrictions were handled by extending the original model, given that the slope parameters were the same in the main regression model and the restrictions.

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Past, Present and Future of Statistical Science II

My review of this book continues with Part II, Reminiscences and personal reflections on career paths.
Ingram Olkin. Reminiscences of the Columbia University Department of Mathematical Statistics in the late 1940s. Full of the names behind the tests, many funny anecdotes.
Herman Chernoff. A career in statistics. Delightful reminiscences of a varied career.
David Brillinger. ” How wonderful the field of statistics is”. Great little read on Tukey’s contributions to statistics.
Juliet Shaffer. Unorthodox journey to statistics. Equity and multiplicity.
Peter Bickel. Statistics before and after my COPSS prize. Statistics really is a lot of fun!
Donna Brogan. The accidental biostatistics professor. Endured astonishing sex discrimination to arrive at a satisfying career.
Bruce Lindsay. Developing a passion for statistics. Funny and enthusiastic reminiscence.
Dennis Cook. Reflections on a statistical career and their implications. The Cook’s distance guy! Very keen on applications.
Kathryn Roeder. Science mixes it up with statistics. Emphasises collaboration.
Jeffrey Rosenthal. Lessons from a twisted career path. Lots of good advice in italics.
Mary Gray. Promoting equity.

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Street-fighting mathematics

Don’t you just love that title for a book? It’s by Sanjoy Mahajan, and reminds me of the statistics course I heard of once called “Extreme experiment design” or something similar.
The first chapter is probably the hardest, but also the one of most relevance to statisticians. It’s on dimensions, and includes the actual reference for the reason why the Mars Orbiter crashed. The other chapters contain some useful tools (a tool = a trick I use twice) for problem solving, namely easy cases, lumping, pictorial proof, taking out the big part, and analogy. reminds me of the little book on proof by Plumpton, Shipton and Perry that was one of my first year maths textbooks. It’s advice included ideas like “can you solve a special case? can you solve it for large n or small n?” … Until the final question “can you do anything at all?”!

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High-throughput detection of biosecurity agents using high resolution melt analysis, next-generation sequencing and protein profiling

Actually, I think this title is even longer than the previous one! Sorelle Bowman also gave the confirmation seminar for her PhD in Forensic Science on 17 September. The biosecurity agents of her project include such nasties as anthrax and plague, ranging from pure strains grown in controlled laboratories to (hopefully agent-free!) samples collected from airport carpets. I’ll be supervising the statistical aspects of Sorelle’s work, which is likely to include both traditional statistical discrimination methods as logistic regression and linear discriminant analysis, through principal components analysis to deal with high-dimensional data, and machine learning methods like decision trees and SVMs. Some of the questions after the talk highlighted issues we’ll have to address such as sensitivity, and underlying population variance. Also we’ll have to clarify interactions between method and agent (will SVMs work better for anthrax and decision trees for plague, for instance) and combinations of methods (will SVMs work better preceded by a PCA or decision tree, for instance). Brett Lidbury and I addressed these combinations of methods in the context of decision trees for laboratory prediction of hepatitis virus in our 2013 BMC Bioinformatics paper.

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The effect of gamma radiation-induced oxidative cell stress on nucler and mitochondrial forensic genotyping

That’s a strong entrant for the talk-with-the-longest-title competition for sure! Corey Goodwin finished his Honours in Forensic Science last year and has embarked on this PhD topic. He gave his confirmation seminar on 17 September. He’ll be comparing nDNA and mDNA damage and forensic profiling capabilities following low and high doses of gamma radiation, using a variety of white-box methods (as in big laboratory machines), as opposed to the so-called black-box methods of algorithmic statistical methods. he seemed to have a good handle on his aims and methods, and I wish him well in his studies.

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Past, Present and Future of Statistical Science I

Wow this handy volume from COPSS has 52 chapters! But they’re generally (a little) shorter and lighter than “Statitsics in Action” reviewed in previous posts. This book I’ll also review in parts, as that’s how it is presented? Part I: The history of COPSS contains just one paper, by Ingram Olkin. Entitled “A brief history of COPSS”, it is a succinct history of 50 years, with complete tables of prizewinners and the like.

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