Notebooks

## Empirical Process Theory

21 Feb 2013 16:26

(I first used the next few paragraphs as part of a review of Pollard's book of lecture notes. I have no shame about self-plagiarism.)

The simplest sort of empirical process arises when trying to estimate a probability distribution from sample data. The difference between the empirical distribution function $F_n(x)$ and the true distribution function $F(x)$ converges to zero everywhere (by the law of large numbers), and — this is non-trivial — the maximum difference between the empirical and true distribution functions converges to zero, too (by the Glivenko-Cantelli theorem, a uniform law of large numbers). The "empirical process" $E_n(x)$ is the re-scaled difference, $n^{1/2} \left[ F_n(x) - F(x) \right]$, and it converges to a Gaussian stochastic process that only depends on the true distribution (by the functional central limit theorem). Empirical process theory is concerned with generalizing this sort of material to other stochastic processes determined by random samples, and indexed by infinite classes (like the real line, or the class of all Borel sets on the line, or some space parameterizing a regression model). The typical objects of concern are proving uniform limit theorems, and with establishing distributional limits. (For instance, one might one want to prove that the errors of all possible regression models in some class will come close to their expected errors, so that maximum-likelihood or least-squares estimation is consistent. [For more on that line of thought, see Sara van de Geer's book.]) This endeavor is closely linked to Vapnik-Chervonenkis-style learning theory, and in fact one can see VC theory as an application of empirical process theory.

As usual, I am most interested in results for dependent data.

• Radoslaw Adamczak, "A tail inequality for suprema of unbounded empirical processes with applications to Markov chains", arxiv:0709.3110
• Donald W. K. Andrews and David Pollard, "An Introduction to Functional Central Limit theorems for Dependent Stochastic Processes", International Statistical Review 62 91994): 119--132 [PDF reprint]
• Patrizia Berti, Irene Crimaldi, Luca Pratelli, and Pietro Rigo, "Rate of convergence of predictive distributions for dependent data", Bernoulli 15 (2009): 1351--1367 [Only for exchangeable sequences, sadly]
• S.G. Bobkov and F. Götze, "Concentration of empirical distribution functions with applications to non-i.i.d. models", Bernoulli 16 (2010): 1385--1414
• Rainer Dahlhaus and Wolfgang Polonik, "Empirical spectral processes for locally stationary time series", Bernoulli 15 (2009): 1--39, arxiv:902.1448
• Herold Dehling (ed.), Empirical Process Techniques for Dependent Data
• Herold Dehling and Olivier Durieu, "Empirical Processes of Multidimensional Systems with Multiple Mixing Properties", arxiv:1004.1088
• Herold Dehling, Olivier Durieu, Marco Tusche, "Empirical Processes of Markov Chains and Dynamical Systems Indexed by Classes of Functions", arxiv:1201.2256
• Herold Dehling, Olivier Durieu and Dalibor Volny, "New Techniques for Empirical Process of Dependent Data", arxiv:0806.2941
• Eustacio del Barrio, Paul Deheuvels and Sara van de Geer, Lectures on Empirical Processes: Theory and Statistical Applications
• P. Doukhan, P. Massart and E. Rio, "Invariance principles for absolutely regular empirical processes", Annales de l'institut Henri Poincaré B 31 (1995): 393--427
• Lutz Duembgen, Perla Zerial, "On Low-Dimensional Projections of High-Dimensional Distributions", arxiv:1107.0417
• Olivier Durieu, Marco Tusche, "An Empirical Process Central Limit Theorem for Multidimensional Dependent Data", arxiv:1110.0963
• Omar El-Dakkak, "Limit Behaviour of Sequential Empirical Measure Processes", arxiv:0810.5565
• James M. Feagin, Weighted Empirical Processes in Dynamic Nonlinear Models
• Robert Hable, "Asymptotic Normality of Support Vector Machines for Classification and Regression", arxiv:1010.0535
• Bruce E. Hansen, "Stochastic Equicontinuity for Unbounded Dependent Heterogeneous Arrays", Econometric Theory 12 (1996): 347--359 [PDF reprint via Prof. Hansen]
• Michael R. Kosorok, Introduction to Empirical Processes and Semiparametric Inference [PDF preprint]
• James Kuelbs, Thomas Kurtz, Joel Zinn, "A CLT for Empirical Processes Involving Time Dependent Data", arxiv:1008.2697
• Johannes C. Lederer, Sara A. van de Geer, "New Concentration Inequalities for Suprema of Empirical Processes", arxiv:1111.3486
• Jean-Francois Marckert, "One more approach to the convergence of the empirical process to the Brownian bridge", arxiv:0710.3296
• D. Marinucci, "The Empirical Process for Bivariate Sequences with Long Memory", Statistical Inference for Stochastic Processes 8 (2005): 205--224
• Shahar Mendelson, Grigoris Paouris, "On generic chaining and the smallest singular value of random matrices with heavy tails", arxiv:1108.3886 ["We present a very general chaining method which allows one to control the supremum of the empirical process $\sup_{h \in H} |N^{-1}\sum_{i=1}^N h^2(X_i)-\E h^2|$ in rather general situations..."]
• Dragan Radulović, Marten Wegkamp, "Uniform Central Limit Theorems for pregaussian classes of functions", pp. 84--102 in Christian Houdré, Vladimir Koltchinskii, David M. Mason and Magda Peligrad (eds.) High Dimensional Probability V: The Luminy Volume
• Richard Samworth and Oliver Johnson, "The empirical process in Mallows distance, with application to goodness-of-fit tests", math.ST/0504424
• Galen R. Shorack and Jon A. Wellner, Empirical Processes with Applications to Statistics
• Michal Talagrand
• Sara van de Geer and Johannes Lederer, "The Bernstein-Orlicz norm and deviation inequalities", arxiv:1111.2450
• Aad W. van der Vaart, Jon A. Wellner
• Weak Convergence and Empirical Processes: With Applications to Statistics
• "Empirical processes indexed by estimated functions", arxiv:0709.1013 ["We consider the convergence of empirical processes indexed by functions that depend on an estimated parameter $\eta$ and give several alternative conditions under which the estimated parameter'' $\eta_n$ can be replaced by its natural limit $\eta_0$ uniformly in some other indexing set $\Theta$"]
• "A local maximal inequality under uniform entropy", Electronic Journal of Statistics 5 (2011): 192--203
• Chao Zhang and Dachen Tao, "Generalization Bound for Infinitely Divisible Empirical Process", Journal of Machine Learning Research Workshops and Conference Proceedings 15 (2011): 864--872

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