The AstroStat Slog

The webcast URL for CAS2010 is at http://www.cfa.harvard.edu/dvlwrap/live/live.ram

The workshop will run from 9:30am-5:30pm EDT on Aug 24 and 25.

The schedule for the mini-Workshop on Computational AstroStatistics is set: http://hea-www.harvard.edu/AstroStat/CAS2010/#schedule

There is an ambitious project afoot to build a 3D map of a meteor stream during the Perseids on Aug 11-12. I got this missive about it from the organizer, Chris Crawford:

This will be one of the better years for Perseids; the moon, which often interferes with the Perseids, will not be a problem this year. So I’m putting together something that’s never been done before: a spatial analysis of the Perseid meteor stream. We’ve had plenty of temporal analyses, but nobody has ever been able to get data over a wide area — because observations have always been localized to single observers. But what if we had hundreds or thousands of people all over North America and Europe observing Perseids and somebody collected and collated all their observations? This is crowd-sourcing applied to meteor astronomy. I’ve been working for some time on putting together just such a scheme. I’ve got a cute little Java applet that you can use on your laptop to record the times of fall of meteors you see, the spherical trig for analyzing the geometry (oh my aching head!) and a statistical scheme that I *think* will reveal the spatial patterns we’re most likely to see — IF such patterns exist. I’ve also got some web pages describing the whole shebang. They start here:

http://www.erasmatazz.com/page78/page128/PerseidProject/PerseidProject.html

I think I’ve gotten all the technical, scientific, and mathematical problems solved, but there remains the big one: publicizing it. It won’t work unless I get hundreds of observers. That’s where you come in. I’m asking two things of you:

1. Any advice, criticism, or commentary on the project as presented in the web pages.
2. Publicizing it. If we can get that ol’ Web Magic going, we could get thousands of observers and end up with something truly remarkable. So, would you be willing to blog about this project on your blog?
3. I would be especially interested in your comments on the statistical technique I propose to use in analyzing the data. It is sketched out on the website here:

http://www.erasmatazz.com/page78/page128/PerseidProject/Statistics/Statistics.html

Given my primitive understanding of statistical analysis, I expect that your comments will be devastating, but if you’re willing to take the time to write them up, I’m certainly willing to grit my teeth and try hard to understand and implement them.

Thanks for any help you can find time to offer.

Chris Crawford

This was written more than a year ago, and I forgot to post it.
Continue reading ‘[Book] The Elements of Statistical Learning, 2nd Ed.’ »

mini-Workshop on Computational Astro-statistics: Challenges and Methods for Massive Astronomical Data
Aug 24-25, 2010
Phillips Auditorium, CfA,
60 Garden St., Cambridge, MA 02138

URL: http://hea-www.harvard.edu/AstroStat/CAS2010
Continue reading ‘mini-Workshop on Computational AstroStatistics [announcement]’ »

Sherpa is a fitting environment in which Chandra data (and really, X-ray data from any observatory) can be analyzed. It has just undergone a major update and now runs on python. Or allows python to run. Something like that. It is a very powerful tool, but I can never remember how to use it, and I have an amazing knack for not finding what I need in the documentation. So here is a little cheat sheet (which I will keep updating as and when if I learn more): Continue reading ‘Everybody needs crampons’ »

From Jogesh Babu:

First Announcement

Summer School in Statistics for Astronomers VI
June 7-12, 2010
with a supplement on Statistics and Computation for Astronomical Surveys
June 12-14, 2010
Registration Deadline: May 3, 2010 or when the enrollment limit reaches.
Penn State University

http://astrostatistics.psu.edu/su10/

Continue reading ‘AstroStat Summer School [Announcement]’ »

I often feel irksome whenever I see a function being normalized over a feasible parameter space and it being used as a probability density function (pdf) for further statistical inference. In order to be a suitable pdf, normalization has to be done over a measurable space not over a feasible space. Such practice often yields biased best fits (biased estimators) and improper error bars. On the other hand, validating a measurable space under physics seems complicated. To be precise, we often lost in translation. Continue reading ‘A short note on Probability for astronomers’ »

Because of blogging and projects I worked on, I happened to collect quite many publications in Astronomy. The collection is biased toward my personal interests. However, these authors discussed statistics in a wide range. So, I felt my astronomical bibliography can be useful to slog audience. Some areas could match your interests. Or your own name can be found. Continue reading ‘astronomy bibliography’ »

Please, IMS Bulletin, v.38 (10) check p.11 of this pdf file for the whole article. Continue reading ‘From Terence’s stuff: You want proof?’ »

When I begin to subscribe arXiv/astro-ph and arXiv/stat, although only for a year I listed astro-ph papers featuring relatively advanced statistics, I also kept more papers relevant to astrostatistics beyond astro-ph or introducing hot topics in statistics and computer science for astronomical data applications. While creating my own arXiv as follows, I had a hope to write up short introductions of statistics that are unlikely known to most of astronomers (like my MADS) and matching subjects/targets in astronomy. I thought such effort could spawn new collaborations or could expand understanding of statistics among astronomers (see Magic Crystal). Well, I couldn’t catch up the growth rate and it’s about time to terminate the hope. However, I thought some papers can be useful to some slog subscribers. I hope they do. Continue reading ‘arxiv list’ »

He was one of the frequently cited statisticians in this slog because of his influence in statistics. It is extremely difficult to avoid his textbooks and his establishment of theoretical statistics when one begins to comprehend and to appreciate the modern theoretical statistics. To me, Testing Statistical Hypotheses and Theory of Point Estimation are two pillars of graduate statistical education. In addition, Elements of Large Sample Theory and Nonparametrics: Statistical Methods Based on Ranks are also eye openers. Continue reading ‘Erich Lehmann’ »

by Emanuel Parzen in Statistical Science 2004, Vol 19(4), pp.652-662 JSTOR

I teach that statistics (done the quantile way) can be simultaneously frequentist and Bayesian, confidence intervals and credible intervals, parametric and nonparametric, continuous and discrete data. My first step in data modeling is identification of parametric models; if they do not fit, we provide nonparametric models for fitting and simulating the data. The practice of statistics, and the modeling (mining) of data, can be elegant and provide intellectual and sensual pleasure. Fitting distributions to data is an important industry in which statisticians are not yet vendors. We believe that unifications of statistical methods can enable us to advertise, “What is your question? Statisticians have answers!”

I couldn’t help liking this paragraph because of its bitter-sweetness. I hope you appreciate it as much as I did.

I was told to stay away from python and I’ve obeyed the order sincerely. However, I collected the following stuffs several months back at the instance of hearing about import inference and I hate to see them getting obsolete. At that time, collecting these modules and getting through them could help me complete the first step toward the quest Learning Python (the first posting of this slog). Continue reading ‘some python modules’ »

As a part of exploring spatial distribution of particles/objects, not to approximate via Poisson process or Gaussian process (parametric), nor to impose hypotheses such as homogenous, isotropic, or uniform, various nonparametric methods somewhat dragged my attention for data exploration and preliminary analysis. Among various nonparametric methods, the one that I fell in love with is tessellation (state space approaches are excluded here). Computational speed wise, I believe tessellation is faster than kernel density estimation to estimate level sets for multivariate data. Furthermore, conceptually constructing polygons from tessellation is intuitively simple. However, coding and improving algorithms is beyond statistical research (check books titled or key-worded partially by computational geometry). Good news is that for computation and getting results, there are some freely available softwares, packages, and modules in various forms. Continue reading ‘[ArXiv] Voronoi Tessellations’ »