ASA statement on p-values

There are many problems with p-values, and I too have chipped in at times. I recently sat in a presentation of an excellent paper, to be submitted to the highest ranked journal in the field. The authors did not conceal their ruthless search for those mesmerizing asterisks indicating significance. I was curious to see many in the crowd are not aware of current history in the making regarding those asterisks.

The web is now swarming with thought-provoking discussions about the recent American Statistical Association (ASA) statement on p-values. Despite their sincere efforts, there are still a lot of back-and-forth over what they actually mean. Here is how I read it.

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Most popular posts – 2015

The top three for the year are:
Out-of-sample data snooping
Code for my yield curve forecasting paper
Review of a couple of books
I personally enjoyed the most writing a few words on ML estimation, and about those great statistical discoveries. Since the last post did not involve any code or images I initially thought it would be a breeze. I in fact spent twice the time I usually do, and it was all good fun.

In 2015 I wrote quite a bit about volatility and correlation. In 2016 I plan to learn more (so to write more) about portfolio construction.

Present-day great statistical discoveries

Some time during the 18th century the biologist and geologist Louis Agassiz said: “Every great scientific truth goes through three stages. First, people say it conflicts with the Bible. Next they say it has been discovered before. Lastly they say they always believed it”. Nowadays I am not sure about the Bible but yeah, it happens.

I express here my long-standing and long-lasting admiration for the following triplet of present-day great discoveries. The authors of all three papers had initially struggled to advance their ideas, which echos the quote above. Here they are, in no particular order.

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How regression statistics mislead experts

This post concerns a paper I came across checking the nominations for best paper published in International Journal of Forecasting (IJF) for 2012-2013. The paper bears the annoyingly irresistible title: “The illusion of predictability: How regression statistics mislead experts”, and was written by Soyer Emre and Robin Hogarth (henceforth S&H). The paper resonates another paper published in “Psychological review” (1973), by Daniel Kahneman and Amos Tversky: “On the psychology of prediction”. Despite the fact that S&H do not cite the 1973 paper, I find it highly related.

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Dark background theme for reading

I am picking up on Rob Hyndman’s suggestion on dark themes for writing. I carried on with a bit of “internet-scientific” reading. Opinions on ‘dark vs white’ background themes, which is better for your eyes, are mixed. You are busy, so just the bottom line: do what works for you.

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R vs MATLAB (Round 3)

At least for me, R by faR. MATLAB has its own way of doing things, which to be honest can probably be defended from many angles. Here are few examples for not so subtle differences between R and MATLAB:

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Quantify your jogging

Numbers are useful (I think we can all agree on that..). If you own a smart phone, you can install this runmeter app. When you run, you can take the smartphone with you and activate this app to collect interesting numbers like distance, pace, fastest pace, heart rate*, calories etc. Now we can load the statistics collected over the past months into R and have a quantified look at the progress.

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