Sep 29th, 2008| 02:15 am | Posted by vlk

Is there an objective method to combine measurements of the same quantity obtained with different instruments?

Suppose you have a set of *N*_{1} measurements obtained with one detector, and another set of *N*_{2} measurements obtained with a second detector. And let’s say you wanted something as simple as an estimate of the mean of the quantity (say the intensity) being measured. Let us further stipulate that the measurement errors of each of the points is similar in magnitude and neither instrument displays any odd behavior. How does one combine the two datasets without appealing to subjective biases about the reliability or otherwise of the two instruments? Continue reading ‘[Q] Objectivity and Frequentist Statistics’ »

Jul 25th, 2008| 01:12 pm | Posted by chasc

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’ »

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2 Comments
Jun 3rd, 2008| 02:53 am | Posted by vlk

It is somewhat surprising that astronomers haven’t cottoned on to Lowess curves yet. That’s probably a good thing because I think people already indulge in smoothing far too much for their own good, and Lowess makes for a very powerful hammer. But the fact that it is semi-parametric and is based on polynomial least-squares fitting does make it rather attractive.

And, of course, sometimes it is unavoidable, or so I told Brad W. When one has too many points for a regular polynomial fit, and they are too scattered for a spline, and too few to try a wavelet “denoising”, and no real theoretical expectation of any particular model function, and all one wants is “a smooth curve, damnit”, then Lowess is just the ticket.

Well, almost.

There is one major problem — *how does one figure what the error bounds are on the “best-fit” Lowess curve?* Clearly, each fit at each point can produce an estimate of the error, but simply collecting the separate errors is not the right thing to do because they would all be correlated. I know how to propagate Gaussian errors in boxcar smoothing a histogram, but this is a whole new level of complexity. Does anyone know if there is software that can calculate reliable error bands on the smooth curve? We will take any kind of error model — Gaussian, Poisson, even the (local) variances in the data themselves.

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11 Comments
May 20th, 2008| 12:10 am | Posted by vlk

Earlier this year, Peter Edmonds showed me a press release that the Chandra folks were, at the time, considering putting out describing the possible identification of a Type Ia Supernova progenitor. What appeared to be an accreting white dwarf binary system could be discerned in 4-year old observations, coincident with the location of a supernova that went off in November 2007 (SN2007on). An amazing discovery, but there is a hitch.

And it is a statistical hitch, and involves two otherwise highly reliable and oft used methods giving contradictory answers at nearly the same significance level! Does this mean that the chances are actually 50-50? Really, we need a bona fide statistician to take a look and point out the errors of our ways.. Continue reading ‘Did they, or didn’t they?’ »

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5 Comments
May 1st, 2008| 02:00 pm | Posted by vlk

*Why is it that detection of emission lines is more reliable than that of absorption lines?*

That was one of the questions that came up during the recent AstroStat Special Session at HEAD2008. When you look at the iconic Figure 1 from Protassov et al (2002), which shows how the null distribution of the Likelihood Ratio Test (LRT) and how it holds up for testing the existence of emission and absorption lines. The thin vertical lines are the nominal F-test cutoffs for a 5% false positive rate. The nominal F-test is too conservative in the former case (figures a and b; i.e., actual existing lines will not be recognized as such), and is too anti-conservative in the latter case (figure c; i.e., non-existent lines will be flagged as real). Continue reading ‘The Flip Test’ »

Jan 21st, 2008| 03:33 pm | Posted by vlk

One of the big problems that has come up in recent years is in how to represent the uncertainty in certain estimates. Astronomers usually present errors as *+-stddev* on the quantities of interest, but that presupposes that the errors are uncorrelated. But suppose you are estimating a multi-dimensional set of parameters that may have large correlations amongst themselves? One such case is that of Differential Emission Measures (DEM), where the “quantity of emission” from a plasma (loosely, how much stuff there is available to emit — it is the product of the volume and the densities of electrons and H) is estimated for different temperatures. See the plots at the PoA DEM tutorial for examples of how we are currently trying to visualize the error bars. Another example is the correlated systematic uncertainties in effective areas (Drake et al., 2005, Chandra Cal Workshop). This is not dissimilar to the problem of determining the significance of a “feature” in an image (Connors, A. & van Dyk, D.A., 2007, SCMA IV). Continue reading ‘Dance of the Errors’ »

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2 Comments
Sep 10th, 2007| 12:15 pm | Posted by vlk

arXiv:0709.1067v1 : Wrong Priors (Carlos C. Rodriguez)

This came through today on astro-ph, suggesting that we could be choosing priors better than we do, and in fact that we generally do a very bad job of it. I have been brought up to believe that, like points in Whose Line Is It Anyway, priors don’t matter (unless you have very little data), so I am somewhat confused. What is going on here?

Jun 8th, 2007| 12:50 am | Posted by vlk

Clauset, Shalizi, & Newman (2007, arXiv/0706.1062) have a very detailed description of what power-law distributions are, how to recognize them, how to fit them, etc. They are also making available their matlab and R codes that they use to do the fitting and such.

Looks like a very handy reference text, though I am a bit uncertain about their use of the K-S test to check whether a dataset can be described with a power-law or not. It is probably fine; perhaps some statisticians would care to comment?