The DOI matches its Crossref record.
Kernel Smoothing
Abstract
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. The basic principle is that local averaging or smoothing is performed with respect to a kernel function.This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilita
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
Cited by
Internet traffic classification using bayesian analysis techniques
Moore, A. W., & Zuev, D. (2005) · Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems · doi:10.1145/1064212.1064220 · Cited by 1,208
Bayesian Neural Networks for Internet Traffic Classification
Auld, T., Moore, A. W., & Gull, S. F. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.883010 · Cited by 494
Internet traffic classification using bayesian analysis techniques
Moore, A. W., & Zuev, D. (2005) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/1071690.1064220 · Cited by 460
Giantomassi, A., Ferracuti, F., Iarlori, S., Ippoliti, G., & Longhi, S. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2370936 · Cited by 168
Anomaly Detection in Images With Smooth Background via Smooth-Sparse Decomposition
Yan, H., Paynabar, K., & Shi, J. (2017) · Technometrics · doi:10.1080/00401706.2015.1102764 · Cited by 111
Metadata
- Authors
- Wand, M. P., & Jones, M. C.
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 1994-12-01
- OpenAlex
- W4235667863
- Cited by
- 763
- References
- 0
- FWCI
- 0.71
- 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: Kernel Smoothing. https://app.cochat.ai/papers/10.1201/b14876
- The paper
- Wand, M. P., & Jones, M. C. (1994). Kernel Smoothing. Chapman & Hall/CRC eBooks. https://doi.org/10.1201/b14876
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.
