Loading [MathJax]/jax/output/CommonHTML/jax.js
Teoriya Veroyatnostei i ee Primeneniya
RUS  ENG    JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB  
General information
Latest issue
Archive
Impact factor
Guidelines for authors
Submit a manuscript

Search papers
Search references

RSS
Latest issue
Current issues
Archive issues
What is RSS



Teor. Veroyatnost. i Primenen.:
Year:
Volume:
Issue:
Page:
Find






Personal entry:
Login:
Password:
Save password
Enter
Forgotten password?
Register


Teoriya Veroyatnostei i ee Primeneniya, 1985, Volume 30, Issue 1, Pages 127–131 (Mi tvp1815)  

This article is cited in 43 scientific papers (total in 43 papers)

Short Communications

Remarks on inequalities for the probabilities of large deviations

I. F. Pinelis, A. I. Sahanenko
Received: 29.06.1983
English version:
Theory of Probability and its Applications, 1986, Volume 30, Issue 1, Pages 143–148
DOI: https://doi.org/10.1137/1130013
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: I. F. Pinelis, A. I. Sahanenko, “Remarks on inequalities for the probabilities of large deviations”, Teor. Veroyatnost. i Primenen., 30:1 (1985), 127–131; Theory Probab. Appl., 30:1 (1986), 143–148
Citation in format AMSBIB
\Bibitem{PinSak85}
\by I.~F.~Pinelis, A.~I.~Sahanenko
\paper Remarks on inequalities for the probabilities of large deviations
\jour Teor. Veroyatnost. i Primenen.
\yr 1985
\vol 30
\issue 1
\pages 127--131
\mathnet{http://mi.mathnet.ru/tvp1815}
\mathscinet{http://mathscinet.ams.org/mathscinet-getitem?mr=779438}
\zmath{https://zbmath.org/?q=an:0583.60023|0568.60028}
\transl
\jour Theory Probab. Appl.
\yr 1986
\vol 30
\issue 1
\pages 143--148
\crossref{https://doi.org/10.1137/1130013}
\isi{https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=Publons&SrcAuth=Publons_CEL&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=A1986A742500013}
Linking options:
  • https://www.mathnet.ru/eng/tvp1815
  • https://www.mathnet.ru/eng/tvp/v30/i1/p127
    Erratum
    This publication is cited in the following 43 articles:
    1. Johannes Milz, Michael Ulbrich, “Sample Size Estimates for Risk-Neutral Semilinear PDE-Constrained Optimization”, SIAM J. Optim., 34:1 (2024), 844  crossref
    2. Phuoc-Truong Huynh, Konstantin Pieper, Daniel Walter, “Towards optimal sensor placement for inverse problems in spaces of measures”, Inverse Problems, 40:5 (2024), 055007  crossref
    3. Yuan Mao, Lei Shi, Zheng-Chu Guo, “Coefficient-based regularized distribution regression”, Journal of Approximation Theory, 297 (2024), 105995  crossref
    4. Emanuele Dolera, Stefano Favaro, Edoardo Mainini, “Strong posterior contraction rates via Wasserstein dynamics”, Probab. Theory Relat. Fields, 2024  crossref
    5. Joubine Aghili, Olga Mula, “An optimal control framework for adaptive neural ODEs”, Adv Comput Math, 50:3 (2024)  crossref
    6. Daniel Zhengyu Huang, Nicholas H. Nelsen, Margaret Trautner, “An operator learning perspective on parameter-to-observable maps”, FoDS, 2024  crossref
    7. Tapio Helin, “Least Squares Approximations in Linear Statistical Inverse Learning Problems”, SIAM J. Numer. Anal., 62:4 (2024), 2025  crossref
    8. Johannes Milz, “Reliable Error Estimates for Optimal Control of Linear Elliptic PDEs with Random Inputs”, SIAM/ASA J. Uncertainty Quantification, 11:4 (2023), 1139  crossref
    9. Johannes Milz, “Sample average approximations of strongly convex stochastic programs in Hilbert spaces”, Optim Lett, 17:2 (2023), 471  crossref
    10. Iosif Pinelis, “Improved concentration bounds for sums of independent sub-exponential random variables”, Statistics & Probability Letters, 191 (2022), 109666  crossref
    11. AbdelKader El Moumen, Salim Benslimane, Samir Rahmani, “Robbins–Monro Algorithm with ψ-Mixing Random Errors”, Math. Meth. Stat., 31:3 (2022), 105  crossref
    12. Enrico Cecini, Ernesto De Vito, Lorenzo Rosasco, “Multi-scale vector quantization with reconstruction trees”, Information and Inference: A Journal of the IMA, 10:3 (2021), 955  crossref
    13. Junhong Lin, Volkan Cevher, “Kernel conjugate gradient methods with random projections”, Applied and Computational Harmonic Analysis, 55 (2021), 223  crossref
    14. Junhong Lin, Alessandro Rudi, Lorenzo Rosasco, Volkan Cevher, “Optimal rates for spectral algorithms with least-squares regression over Hilbert spaces”, Applied and Computational Harmonic Analysis, 48:3 (2020), 868  crossref
    15. Abhishake Rastogi, Gilles Blanchard, Peter Mathé, “Convergence analysis of Tikhonov regularization for non-linear statistical inverse problems”, Electron. J. Statist., 14:2 (2020)  crossref
    16. Weinan E, Jiequn Han, Qianxiao Li, “A mean-field optimal control formulation of deep learning”, Res Math Sci, 6:1 (2019)  crossref
    17. Gilles Blanchard, Oleksandr Zadorozhnyi, “Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods”, Bernoulli, 25:4B (2019)  crossref
    18. Zheng-Chu Guo, Lei Shi, “Optimal rates for coefficient-based regularized regression”, Applied and Computational Harmonic Analysis, 47:3 (2019), 662  crossref
    19. Kartashov A.S., Sakhanenko A.I., “On Sufficient Conditions For a Gaussian Approximation of Kernel Estimates For Distribution Densities”, Sib. Electron. Math. Rep., 15 (2018), 1530–1552  crossref  isi
    20. Antoine Marchina, “Concentration inequalities for separately convex functions”, Bernoulli, 24:4A (2018)  crossref
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Теория вероятностей и ее применения Theory of Probability and its Applications
    Statistics & downloads:
    Abstract page:589
    Full-text PDF :317
     
      Contact us:
     Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2025