Archive for the ‘Stat’ Category.

[AAS-HEAD 2011] Time Series in High Energy Astrophysics

We organized a Special Session on Time Series in High Energy Astrophysics: Techniques Applicable to Multi-Dimensional Analysis on Sep 7, 2011, at the AAS-HEAD conference at Newport, RI. The talks presented at the session are archived at http://hea-www.harvard.edu/AstroStat/#head2011

A tremendous amount of information is contained within the temporal variations of various measurable quantities, such as the energy distributions of the incident photons, the overall intensity of the source, and the spatial coherence of the variations. While the detection and interpretation of periodic variations is well studied, the same cannot be said for non-periodic behavior in a multi-dimensional domain. Methods to deal with such problems are still primitive, and any attempts at sophisticated analyses are carried out on a case-by-case basis. Some of the issues we seek to focus on are methods to deal with are:
* Stochastic variability
* Chaotic Quasi-periodic variability
* Irregular data gaps/unevenly sampled data
* Multi-dimensional analysis
* Transient classification

Our goal is to present some basic questions that require sophisticated temporal analysis in order for progress to be made. We plan to bring together astronomers and statisticians who are working in many different subfields so that an exchange of ideas can occur to motivate the development of sophisticated and generally applicable algorithms to astronomical time series data. We will review the problems and issues with current methodology from an algorithmic and statistical perspective and then look for improvements or for new methods and techniques.

[announce] upcoming workshops and conferences

Kirk Borne has compiled a list of interesting workshops and conferences coming up in the near future:

The Future of Scientific Knowledge Discovery in Open Networked Environments
http://sites.nationalacademies.org/PGA/brdi/PGA_060422

New York Workshop on Computer, Earth, and Space Sciences 2011
http://www.giss.nasa.gov/meetings/cess2011/

Innovations in Data-Intensive Astronomy
http://www.nrao.edu/meetings/bigdata/

Astrostatistics and Data Mining in Large Astronomical Databases
http://www.iwinac.uned.es/Astrostatistics/

Statistical Challenges in Modern Astronomy V (including summer school & tutorials)
http://astrostatistics.psu.edu/su11scma5/

Very Wide Field Surveys in the Light of Astro2010
http://widefield2011.pha.jhu.edu/

Statistical Methods for Very Large Datasets
http://www.regonline.com/builder/site/Default.aspx?eventid=757633

23rd Scientific and Statistical Database Management Conference
http://ssdbm2011.ssdbm.org/

International Statistical Institute (ISI) World Congress
http://www.isi2011.ie/

NASA Conference on Intelligent Data Understanding
https://c3.ndc.nasa.gov/dashlink/projects/43/

[announce] SCMA V

via David van Dyk, information about 3 events in astrostatistics hosted by Penn State’s Center for Astrostatistics:

  1. Summer School in Statistics for Astronomers VII (June 6-10, 2011)
  2. Pre-conference Tutorials (June 11-12, 2011)
  3. Statistical Challenges in Modern Astronomy V (June 13-17, 2011)*

*Web site: http://astrostatistics.psu.edu/su11scma5/

Registration is now open until May 6
(Summer School registration may close earlier if the enrollment limit is reached)

Contributed papers for the SCMA V conference are welcome

Summer School in Statistics for Astronomers: The seventh summer school is an intensive week covering basic statistical inference, several fields of applied statistics, and hands-on experience with the R computing environment. Topics include: exploratory data analysis, hypothesis testing, parameter estimation, regression, bootstrap resampling, model selection & goodness-of-fit, maximum likelihood and Bayesian methods, nonparametrics, spatial processes, and times series. Instructors are mostly faculty members in statistics.

Pre-conference tutorials: Instruction in four areas of astrostatistical interest presented during the weekend between the Summer School and SCMA V conference. Topics are: Bayesian computation and MCMC; data mining; R for astronomers; and wavelets for image analysis. Instructors are members of the SCMA V Scientific Organizing Committee.

SCMA V conference: Held every five years, SCMA conferences are the premier cross-disciplinary forum for research statisticians and astronomers to discuss methodological issues of mutual interest. Session topics include: statistical modeling in astronomy, Bayesian analysis across astronomy; Bayesian cosmology; data mining and informatics; sparsity; interpreting astrophysical simulations; time domain astronomy; spatial and image analysis; and future directions for astrostatistics. Invited lectures will be followed by cross-disciplinary commentaries. The conference welcomes contributed papers from statisticians and astronomers.

Visit http://astrostatistics.psu.edu/su11scma5/ for more information and registration

Contacts:
Eric Feigelson, Dept. of Astronomy & Astrophysics, Penn State, edf@astro.psu.edu
G. Jogesh Babu, Dept. of Statistics, Penn State, babu@stat.psu.edu

coin toss with a twist

Here’s a cool illustration of how to use Bayesian analysis in the limit of very little data, when inferences are necessarily dominated by the prior. The question, via Tom Moertel, is: suppose I tell you that a coin always comes up heads, and you proceed to toss it and it does come up heads — how much more do you believe me now?

He also has the answer worked out in detail.

(h/t Doug Burke)

Yes, please

CAS 2010

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

The Perseid Project [Announcement]

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

[Book] The Elements of Statistical Learning, 2nd Ed.

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 AstroStatistics [announcement]

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]’ »

Everybody needs crampons

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’ »

AstroStat Summer School [Announcement]

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]’ »

A short note on Probability for astronomers

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’ »

astronomy bibliography

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’ »

From Terence’s stuff: You want proof?

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?’ »

arxiv list

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’ »