Well, not all of them. Yet, presentations by those with familiar names to astronomers, like Donoho, Bickel, Murtagh, Candes, Jin, Spiegelhalter, McLachlan, Hall, and Rice are available. More presentations by personally worshiped statisticians are also up. Some are astrostatistics talks topic-wise or example-wise. Some are quite off from astronomical ground because of their large p and small n problems. If you have some spare time and want to learn top notch statistics handling high dimensional data, time can be well spent with these web seminars (a link for the seminar list).

• [stat.ME:0805.2756] Fionn Murtagh
  The Remarkable Simplicity of Very High Dimensional Data: Application of Model-Based Clustering

• [astro-ph:0805.2945] Martin, de Jong, & Rix
  A comprehensive Maximum Likelihood analysis of the structural properties of faint Milky Way satellites

• [astro-ph:0805.2946] Kelly, Fan, & Vestergaard
  A Flexible Method of Estimating Luminosity Functions [my subjective comment is added at the bottom]

• [stat.ME:0805.3220] Bayarri, Berger, Datta
  Objective Bayes testing of Poisson versus inflated Poisson models (will it be of use when one is dealing with many zero background counts, underpopulated above zero background counts, and underpopulated source counts?)

[Comment] You must read it. It can serve as a very good Bayesian tutorial for astronomers. I think there’s a typo, nothing major, plus/minus sign in the likelihood, though. Tom Loredo kindly has informed through his extensive slog comments about Schechter function and this paper made me appreciate the gamma distribution more. Schechter function and the gamma density function share the same equation although the objective of their use does not have much to be shared (Forgive my Bayesian ignorance in the extensive usage of gamma distribution except the fact it’s a conjugate of Poisson or exponential distribution).

FYI, there was another recent arxiv paper on zero-inflation [stat.ME:0805.2258] by Bhattacharya, Clarke, & Datta
A Bayesian test for excess zeros in a zero-inflated power series distribution

To my knowledge, random matrix received statisticians’ interests fairly recently and SAMSI (Statistical and Applied Mathematical Sciences Institute) offered a semester long program on High Dimensional Inference and Random Matrices (tutorials and lecture notes can be found) during Fall 2006 . However, my knowledge is very limited to make a comment or critic on Kim and Liddle’s paper. Clearly, nonetheless, this paper is not about random matrix theory but about its straightforward application to the cosmological model viability.

A. Liddle has published papers on theoretic cosmology from a statistical model based approach (the ones I’ve seen are most likely related to statistical model selection). Personally, I like his book: An Introduction to Modern Cosmology (2nd ed. ISBN 0-470-84835-9), which might be useful to statisticians who wish to work with cosmologists.