Books with good coverage of joint distributions, multivariate statistics, etc? I searched on the internet for books on statistics (particularly 4shared.com), and most of the books I found do not cover multivariate statistics in detail. Are there any good books which cover these topics in detail and with sufficient examples? 
 A: Last year, I spent every lunchtime for a week going to the Waterstones University bookshop in London looking for a good book on multivariate statistics (sad I know!). I also endorse Izenman, Modern Multivariate Statistical Techniques, Springer 2008, as it really was the stand-out book. It starts every chapter with an easy to understand outline and gradually progresses through the very detailed theory with lots of real world examples, data sources and visualisations. It's actually quite a good read for the basic principles and you come back later when the going gets heavy.
A: Despite @whuber's sound comment--covering all advances in MV analysis for the last 30 years is also outside the scope of e.g. the famous Handbook of Statistics series--, I would like to recommend 

Izenman, Modern Multivariate
  Statistical Techniques, Springer
  2008.

Although it has pretty much the same coverage than the Elements of Statistical Learning, from Hastie and coll., it has some different applications and covers extra topic, like Correspondence Analysis. There is a short review by John Maindonald in the JSS.
