I'm a professional statistician - or possibly a data scientist - managing an analytics team in a New Zealand government Ministry. Most of my career has been in management and evaluation of overseas aid programmes, but since 2011 I have been working on a range of economic data issues in New Zealand.
My interests include economics, econometrics, complex surveys, time series, spatial statistics, text mining, R, data visualisation, and anything that involves putting data and analysis in the hands of as broad a range of people as possible.
The views expressed in this blog are very definitely my own, not my organisation or the New Zealand government. I might be blogging about data and analysis issues that crop up in work, but anything controversial that relates to my work has crept in by mistake and will be removed if and when I notice it.
- Comments on specific posts please use the comments section.
- This page is hosted by GitHub and the source code is all available. The master branch just has the version by Jekyll; you need to check out the source branch to see the actual stuff I write.
- If you want to make a general comment or request about the blog, feel free to create an issue there and I will definitely respond one way or another.
- If you're interested in this blog you might as well follow me on Twitter.
- Professional contacts should be able to find me on my work email or on LinkedIn easily enough.
- Personal friends should be able to find me on Facebook.
I also host on my GitHub page the forecastHybrid package, which makes it easy to estimate ensemble or hybrid forecasts using a range of the methods in Rob Hyndman's forecast package. I'm a contributor to that package but David Shaub is the primary author and maintainer.
Blogs I read
- R-bloggers aggregation of more than 500 blogs on R and statistics, including mine.
- StatsBlogs syndicates posts from statistics related blogs and brings traffic and user interaction to contributing blogs.
- Statistical Thinking by Frank Harrell, author of Regression Modeling Strategies which is one of my top ten books in this field.
- Rob Hyndman's Hyndsight, focused on time series forecasting.
- Thomas Lumley's StatsChat, aimed at general audiences interested in statistical issues with a focus on things like constructive criticism of media coverage.
- Thomas Lumley's "Biased and Inefficient", aimed at a more technical audience on topics that doesn't fit into StatsChat.
- RStudio's blog, useful particularly for announcements about the large number of useful R packages the RStudio team own or contribute to, and announcements about RStudio, Shiny, and their Server versions.
- Andrew Gelman on Statistical Modelling, Causal Inference, and Social Science.
- David's AdVentures in Data.
- Gavin Simpson's From the Bottom of the Heap.
- Timely Portfolio
- Microsoft Revolutions
There is also this post about my ten favourite and twelve second-favourite statistics / data science books.