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/

Abstracts are due Sep 17.

Meeting Justification

We propose to highlight the growing use of ‘non-parametric’ techniques to distill meaningful science from today’s astronomical data. Challenges range from Kuiper objects to cosmology. We have chosen just a few ‘teaching’ examples from this lively interdisciplinary area.

Meeting Notes

This ‘Astro-Statistics’ special session is proposed in concert with an ‘Astro-Informatics’ Special Session, organized by Kirk Bourne. In this proposed ‘Non-Parametrics for the Non-Specialist’ session, we are highlighting just a few of the new, outstanding, applications. Many are coming to fruition just now, in this age of large data-sets, complex instruments, and subtleties of distilling accurate science from indirect measurements. We chose to highlight: complex models (cosmology, black hole mass distributions); and complex data, such as image (spatial); and timing analyses (e.g. transients such as the distribution of Kuiper objecs) from surveys. We invited a mixture of newer and seasoned speakers; and ones that will make good ‘teaching examples’. At the same time, we left out many new areas. Hence we are planning a lively, associated, poster session. The format will be: An Intro by one of the seasoned statisticians; followed by ‘examples’ talks by two astronomers and a physicist. Following, another of the senior statisticians will discuss the principles. Finally, a senior astrophysicist will summarize challenges for the future. We plan to leave time for one-minute poster advertisements highlighting other areas. Expected participants include: Eric Feigelson, Brandon Kelly, Meyer Pesenson, Stanislav (George) Djorgovski, Tom Loredo, Alanna Connors, Pavlos Protopapas, Katrin Heitmann, Chad Schaefer, Xiao Li Meng.

Nevertheless, I’ve noticed a few astronomers rigorously apply principle component analysis (PCA) in order to reduce the dimensionality of a data set. An evident example of PCA applications in astronomy is photo-z. In contrast to the wide PCA application, almost no publication about statistical adequacy studies is found by investigating the properties of covariance matrix and its estimation method particularly when it is sparse. Even worse, the notion of measurement errors are improperly implemented since statistician’s dimension reduction methodology never confronted astronomers’ measurement errors. How to choose components is seldom discussed since the significance in physics model is rarely agreeing with statistical significance. This disagreement often elongates scientific writings hard to please readers. As a compromise, statistical parts are omitted, which makes me feel the publication incomplete.

Due to its easy visualization via intuitive scales, in wavelet multiscale imaging, the coarse scale to fine scale approach and the assumption of independent noise enables to clean the noisy image and to accentuate features in it. Likewise, principle components and other dimension reduction methods in statistics capture certain features via transformed metrics and regularized, or penalized objective functions. These features are not necessary to match the important features in astrophysics unless the likelihood function and selected priors match physics models. To my knowledge, astronomical literature exploiting PCA for dimension reduction for prediction rarely explains why PCA is chosen for dimensionality reduction, how to compensate the sparsity in covariance matrix, and other questions, often the major topics in bioinformatics. In the literature, these questions are explored to explain the particular selection of gene attributes or bio-markers under a certain response like blood pressures and types of cancers. Instead of binning and chi-square minimization, statisticians explore strategies how to compensate sparsity in the data set to get unbiased best fits and righteous error bars based on data matching assumptions and theory.

Luckily, there are efforts among some renown astronomers to form a community of astroinformatics. At the dawn of bioinformatics, genetic scientists were responsible for the bio part and statisticians were responsible for the informatics until young scientists are educated enough to carry out bioinformatics by themselves. Observing this trend partially from statistics conferences created an urge in me that it is my responsibility to ponder why there has been shortage of statisticians’ involvement in astronomy regardless of plethora of catalogs and data archives with long history. A few postings will follow what I felt while working among astronomers. I hope this small bridging effort to narrow the gap between two communities. My personal wish is to see prospering astroinformatics like bioinformatics.