The problem with data analysis is of course that it is a performing art. It is not something you easily write a paper on; rather, it is something you do. And so it is difficult to publish.

quoted from this conversation ——————————————————-

Statistical Science has a nice “conversations” series with renown statisticians. This series always benefits me because of 1. learning the history of statistics through a personal life, 2. confronting various aspects in statistics as many statisticians as were interviewed, and 3. acquiring an introductory education in the statistics that those interviewees have perfected over many years in a plain language. One post in the slog from this series was a conversation with Leo Breiman about the two cultures in statistical modeling. Because of Prof. Huber’s diverse experiences and many contributions in various fields, this conversation may entertain astronomers and computer scientists as well as statisticians.

The dialog is available through arxiv.org: [stat.ME:0808.0777] written by Andreas Buja, Hans R. Künsch.

He became famous due to his early year paper in robust statistics titled, Robust Estimation of a Location Parameter but I see him as a pioneer in data mining, laying a corner stone for massive/multivariate data analysis when computers were not as much capable as today’s. His book, Robust Statistics (Amazon link) and the paper Projection Pursuit in Annals of Statistics (Vol. 13, No. 2, pp. 435-475, yr. 1985) are popular among many well known publications.

He has publications in geoscience and Babylonian astronomy. This conversation includes names like Steven Weinberg, the novel laureate (The First Three Minutes is a well known general science book) and late Carl Sagan (famous for books/a movie like Cosmos and Contact) showing his extent scholarly interests and genius beyond statistics. At the beginning, I felt like learning the history of computation and data analysis apart from statistics.

I’m glad to see this week presented a paper that I had dreamed of many years ago in addition to other interesting papers. Nowadays, I’m more and more realizing that astronomical machine learning is not simple as what we see from machine learning and statistical computation literature, which typically adopted data sets from the data repository whose characteristics are well known over the many years (for example, the famous iris data; there are toy data sets and mock catalogs, no shortage of data sets of public characteristics). As the long list of authors indicates, machine learning on astronomical massive data sets are never meant to be a little girl’s dream. With a bit of my sentiment, I offer the list of this week:

• [astro-ph:0804.4068] S. Pires et al.
  FASTLens (FAst STatistics for weak Lensing) : Fast method for Weak Lensing Statistics and map making
• [astro-ph:0804.4142] M.Kowalski et al.
  Improved Cosmological Constraints from New, Old and Combined Supernova Datasets
• [astro-ph:0804.4219] M. Bazarghan and R. Gupta
  Automated Classification of Sloan Digital Sky Survey (SDSS) Stellar Spectra using Artificial Neural Networks
• [gr-qc:0804.4144]E. L. Robinson, J. D. Romano, A. Vecchio
  Search for a stochastic gravitational-wave signal in the second round of the Mock LISA Data challenges
• [astro-ph:0804.4483]C. Lintott et al.
  Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
• [astro-ph:0804.4692] M. J. Martinez Gonzalez et al.
  PCA detection and denoising of Zeeman signatures in stellar polarised spectra
• [astro-ph:0805.0101] J. Ireland et al.
  Multiresolution analysis of active region magnetic structure and its correlation with the Mt. Wilson classification and flaring activity

A relevant post related machine learning on galaxy morphology from the slog is found at svm and galaxy morphological classification

< Added: 3rd week May 2008>[astro-ph:0805.2612] S. P. Bamford et al.
Galaxy Zoo: the independence of morphology and colour