The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
Uniform Central Limit Theorems
Abstract
This book shows how the central limit theorem for independent, identically distributed random variables with values in general, multidimensional spaces, holds uniformly over some large classes of functions. The author, an acknowledged expert, gives a thorough treatment of the subject, including several topics not found in any previous book, such as the Fernique-Talagrand majorizing measure theorem for Gaussian processes, an extended treatment of Vapnik-Chervonenkis combinatorics, the Ossiander L2 bracketing central limit theorem, the Giné-Zinn bootstrap central limit theorem in probability, the Bronstein theorem on approximation of convex sets, and the Shor theorem on rates of convergence over lower layers. Other results of Talagrand and others are surveyed without proofs in separate sections. Problems are included at the end of each chapter so the book can be used as an advanced text. The book will interest mathematicians working in probability, mathematical statisticians and computer scientists working in computer learning theory.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
An introduction to probability theory and its applications
(1958) · Journal of the Franklin Institute · doi:10.1016/0016-0032(58)90688-4 · Cited by 29,269
Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602
Convergence of Probability Measures
Billingsley, P. (1999) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316962 · Cited by 13,910
Harary, F. (1969) · Cited by 10,739
Garreau, G. A., & Rudin, W. (1987) · The Statistician · doi:10.2307/2348852 · Cited by 10,622
Probability Inequalities for sums of Bounded Random Variables
Hoeffding, W. (1994) · Springer Series in Statistics · doi:10.1007/978-1-4612-0865-5_26 · Cited by 6,666
Introduction to Fourier Analysis on Euclidean Spaces.
Stein, E. M., & Weiss, G. (1971) · Cited by 5,080
Weak Convergence and Empirical Processes
van der Vaart, A. W., & Wellner, J. A. (1996) · Springer Series in Statistics · doi:10.1007/978-1-4757-2545-2 · Cited by 5,014
On the mathematical foundations of theoretical statistics
Fisher, R. A. (1922) · Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character · doi:10.1098/rsta.1922.0009 · Cited by 4,039
Cited by
Concentration Inequalities: A Nonasymptotic Theory of Independence
Boucheron, S., Lugosi, G., & Massart, P. (2013) · doi:10.1093/acprof:oso/9780199535255.001.0001 · Cited by 1,768
Concentration Inequalities and Model Selection
(2007) · Lecture Notes in Mathematics · doi:10.1007/978-3-540-48503-2 · Cited by 744
Consistency of spectral clustering
von Luxburg, U., Belkin, M., & Bousquet, O. (2008) · The Annals of Statistics · doi:10.1214/009053607000000640 · Cited by 540
An Introduction to Computational Stochastic PDEs
Lord, G. J., Powell, C. E., & Shardlow, T. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139017329 · Cited by 537
Introduction to Statistical Learning Theory
Bousquet, O., Boucheron, S., & Lugosi, G. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-28650-9_8 · Cited by 503
Metadata
- Authors
- Dudley, R. M.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 1999-07-28
- OpenAlex
- W1507545330
- Cited by
- 785
- References
- 94
- FWCI
- 3.80
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Uniform Central Limit Theorems. https://app.cochat.ai/papers/10.1017/cbo9780511665622
- The paper
- Dudley, R. M. (1999). Uniform Central Limit Theorems. Cambridge University Press eBooks. https://doi.org/10.1017/cbo9780511665622
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.