The DOI matches its Crossref record.
Stable Non-Gaussian Random Processes: Stochastic Models with Infinite Variance: Stochastic Modeling
Abstract
This book serves as a standard reference, making this area accessible not only to researchers in probability and statistics, but also to graduate students and practitioners. The book assumes only a first-year graduate course in probability. Each chapter begins with a brief overview and concludes with a wide range of exercises at varying levels of difficulty. The authors supply detailed hints for the more challenging problems, and cover many advances made in recent years.
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
On Bayesian Modeling of Fat Tails and Skewness
Fernández, C., & Steel, M. F. J. (1998) · Journal of the American Statistical Association · doi:10.1080/01621459.1998.10474117 · Cited by 1,083
EEG microstate sequences in healthy humans at rest reveal scale-free dynamics.
Van De Ville, D., Britz, J., & Michel, C. M. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1007841107 · Cited by 606
Is Network Traffic Appriximated by Stable Lévy Motion or Fractional Brownian Motion?
Mikosch, T., Resnick, S., Rootzén, H., & Stegeman, A. (2002) · The Annals of Applied Probability · doi:10.1214/aoap/1015961155 · Cited by 270
Jessen, H., & Mikosch, T. (2006) · Publications de l'Institut Mathematique · doi:10.2298/pim0694171j · Cited by 236
Davis, A. B., & Marshak, A. (2010) · Reports on Progress in Physics · doi:10.1088/0034-4885/73/2/026801 · Cited by 89
Metadata
- Authors
- Samorodnitsky, G., & Taqqu, M. S.
- Venue
- Routledge eBooks
- Publisher
- Routledge
- Published
- 2017-11-22
- OpenAlex
- W2022894179
- Cited by
- 3,802
- References
- 0
- FWCI
- 49.76
- 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: Stable Non-Gaussian Random Processes: Stochastic Models with Infinite Variance: Stochastic Modeling. https://app.cochat.ai/papers/10.1201/9780203738818
- The paper
- Samorodnitsky, G., & Taqqu, M. S. (2017). Stable Non-Gaussian Random Processes: Stochastic Models with Infinite Variance: Stochastic Modeling. Routledge eBooks. https://doi.org/10.1201/9780203738818
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.
