The AstroStat Slog

Student’s t-distribution is somewhat underrepresented in the astronomical community. Having an article with nice stories, it looks to me the best way to introduce the t distribution. This article describing historic anecdotes about monumental statistical developments occurred about 100 years ago.

Guinness, Gosset, Fisher, and Small Samples by Joan Fisher Box
Source: Statist. Sci. Volume 2, Number 1 (1987), 45-52.

No time for reading the whole article? I hope you have a few minutes to read following quotes, which are quite enchanting to me. Continue reading ‘Guinness, Gosset, Fisher, and Small Samples’ »

You would think that something like “measurement error” is a well-defined concept, and everyone knows what it means. Not so. I have so far counted at least 3 different interpretations of what it means.

Suppose you have measurements X={X_i, i=1..N} of a quantity whose true value is, say, X₀. One can then compute the mean and standard deviation of the measurements, E(X) and σ_X. One can also infer the value of a parameter θ(X), derive the posterior probability density p(θ|X), and obtain confidence intervals on it.

So here are the different interpretations:

1. Measurement error is σ_X, or the spread in the measurements. Astronomers tend to use the term in this manner.
2. Measurement error is X₀-E(X), or the “error made when you make the measurement”, essentially what is left over beyond mere statistical variations. This is how statisticians seem to use it, essentially the bias term. To quote David van Dyk
   

   For us it is just English. If your measurement is different from the real value. So this is not the Poisson variability of the source for effects or ARF, RMF, etc. It would disappear if you had a perfect measuring device (e.g., telescope).

3. Measurement error is the width of p(θ|X), i.e., the measurement error of the first type propagated through the analysis. Astronomers use this too to refer to measurement error.

Who am I to say which is right? But be aware of who you may be speaking with and be sure to clarify what you mean when you use the term!

Diab Jerius follows up on LOESS techniques with a very nice summary update and finds LOCFIT to be very useful, but there are still questions about how it deals with measurement errors and combining observations from different experiments:

Continue reading ‘loess and lowess and locfit, oh my’ »