Welcome to the probability inequality mines!
When something in your process (measurement, estimation) means that you can be pretty sure that a whole bunch of your stuff is damn likely to be somewhere in particular.
This is basic workhorse stuff in univariate probability, and turns out to be yet more essential in multivariate matrix probability, as seen in matrix factorisation, compressive sensing, PAC-bounds and suchlike.
- Dasgupta, Asymptotic Theory of Satstics and Proboability (Dasg08) is very easy, and despite its name introduces some nice basic non-asymptotic inequalities
- Raginsky Sason, Concentration of Measure Inequalities in Information Theory, Communications and Coding (RaSa12)
- Tropp, An Introduction to Matrix Concentration Inequalities (Trop15) high-dimensional data! free!
- Boucheron, Bousquet & Lugosi, Concentration inequalities (BoBl04a) (Clear and brisk but missing some newer stuff)
- Massart, Concentration inequalities and model section (Mass07). Clear, and focussed, but very quick and further, depressingly, by being applied it also demonstrates the limitations of these techniques. Mass00 is an earlier draft.
- Boucheron, Lugosi & Massart, Concentration inequalities: a nonasymptotic theory of independence (BoLM13). Haven’t read it yet.
Foundational but impenetrable things I won’t read right now: Talagrand’s opus that is commonly credited with kicking off the modern fad especially with the chaining method. (Tala95)
These are everywhere in statistics. Special attention will be given here to finite-sample inequalities. Asymptotic normality is so last season. These days we care about finite sample performance, and asymptotic results don’t help us there. Apparently I can construct useful bounds using concentration inequalities? One suggested keyword to disambiguate: Ahlswede-Winterfeld bounds?
Large deviation inequalities, empirical process inequalities, Talagrand chaining method. Berry-Esseen bound.
Nikhil Srivastava’s Discrepancy, Graphs, and the Kadison-Singer Problem has a wonderful explanation of Ahlswede-Winter-style matrix Chernoff Bounds. Gros11 is also readable, and gives quantum-mechanical results (i.e. the matrices are complex-valued).
In recent years, random matrices have come to play a major role in computational mathematics, but most of the classical areas of random matrix theory remain the province of experts. Over the last decade, with the advent of matrix concentration inequalities, research has advanced to the point where we can conquer many (formerly) challenging problems with a page or two of arithmetic.
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