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Teoriya Veroyatnostei i ee Primeneniya, 1975, Volume 20, Issue 4, Pages 738–754 (Mi tvp3338)  

This article is cited in 41 scientific papers (total in 41 papers)

On efficiency of a class of non-parametric estimates

B. Ya. Levit

Moscow
Abstract: For a general class of statistics Tn defined by a relation of the form
i=1nψ(Xi,Tn)=0,
where Xi are observations, a number of results is proved which show that Tn (or, in some cases, their appropriate modifications Tn) are locally asymptotically minimax estimates of the corresponding functional Φ(F) of the unknown distribution F provided the family of all admissible distributions F is sufficiently large.
Received: 06.12.1973
English version:
Theory of Probability and its Applications, 1976, Volume 20, Issue 4, Pages 723–740
DOI: https://doi.org/10.1137/1120081
Bibliographic databases:
Language: Russian
Citation: B. Ya. Levit, “On efficiency of a class of non-parametric estimates”, Teor. Veroyatnost. i Primenen., 20:4 (1975), 738–754; Theory Probab. Appl., 20:4 (1976), 723–740
Citation in format AMSBIB
\Bibitem{Lev75}
\by B.~Ya.~Levit
\paper On efficiency of a~class of non-parametric estimates
\jour Teor. Veroyatnost. i Primenen.
\yr 1975
\vol 20
\issue 4
\pages 738--754
\mathnet{http://mi.mathnet.ru/tvp3338}
\mathscinet{http://mathscinet.ams.org/mathscinet-getitem?mr=403052}
\zmath{https://zbmath.org/?q=an:0367.62041}
\transl
\jour Theory Probab. Appl.
\yr 1976
\vol 20
\issue 4
\pages 723--740
\crossref{https://doi.org/10.1137/1120081}
Linking options:
  • https://www.mathnet.ru/eng/tvp3338
  • https://www.mathnet.ru/eng/tvp/v20/i4/p738
  • This publication is cited in the following 41 articles:
    1. Vladimir Koltchinskii, “Estimation of smooth functionals of covariance operators: Jackknife bias reduction and bounds in terms of effective rank”, Ann. Inst. H. Poincaré Probab. Statist., 61:1 (2025)  crossref
    2. Tijana Zrnic, Emmanuel J. Candès, “Cross-prediction-powered inference”, Proc. Natl. Acad. Sci. U.S.A., 121:15 (2024)  crossref
    3. HUI CHEN, WINSTON WEI DOU, LEONID KOGAN, “Measuring “Dark Matter” in Asset Pricing Models”, The Journal of Finance, 79:2 (2024), 843  crossref
    4. Vladimir Koltchinskii, Martin Wahl, Progress in Probability, 80, High Dimensional Probability IX, 2023, 393  crossref
    5. Oluwagbenga David Agboola, Han Yu, “Neighborhood-based cross fitting approach to treatment effects with high-dimensional data”, Computational Statistics & Data Analysis, 186 (2023), 107780  crossref
    6. Dylan J. Foster, Vasilis Syrgkanis, “Orthogonal statistical learning”, Ann. Statist., 51:3 (2023)  crossref
    7. Vladimir Koltchinskii, “Estimation of smooth functionals in high-dimensional models: Bootstrap chains and Gaussian approximation”, Ann. Statist., 50:4 (2022)  crossref
    8. Bruce E. Hansen, “A Modern Gauss–Markov Theorem”, ECTA, 90:3 (2022), 1283  crossref
    9. Vladimir Koltchinskii, Mayya Zhilova, “Estimation of smooth functionals in normal models: Bias reduction and asymptotic efficiency”, Ann. Statist., 49:5 (2021)  crossref
    10. Vladimir Koltchinskii, Mayya Zhilova, “Efficient estimation of smooth functionals in Gaussian shift models”, Ann. Inst. H. Poincaré Probab. Statist., 57:1 (2021)  crossref
    11. Richard Berk, Andreas Buja, Lawrence Brown, Edward George, Arun Kumar Kuchibhotla, Weijie Su, Linda Zhao, “Assumption Lean Regression”, The American Statistician, 75:1 (2021), 76  crossref
    12. Vladimir Koltchinskii, Mayya Zhilova, “Estimation of Smooth Functionals of Location Parameter in Gaussian and Poincaré Random Shift Models”, Sankhya A, 83:2 (2021), 569  crossref
    13. Prosper Dovonon, Yves F. Atchadé, “Efficiency bounds for semiparametric models with singular score functions”, Econometric Reviews, 39:6 (2020), 612  crossref
    14. Chris A.J. Klaassen, Nanang Susyanto, “Semiparametrically efficient estimation of Euclidean parameters under equality constraints”, Journal of Statistical Planning and Inference, 201 (2019), 120  crossref
    15. Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins, “Double/debiased machine learning for treatment and structural parameters”, The Econometrics Journal, 21:1 (2018), C1  crossref
    16. Marco Carone, Iván Díaz, Mark J. van der Laan, Springer Series in Statistics, Targeted Learning in Data Science, 2018, 483  crossref
    17. Sherri Rose, Mark J. van der Laan, Springer Series in Statistics, Targeted Learning in Data Science, 2018, 3  crossref
    18. Laura B. Balzer, Mark J. van der Laan, Maya L. Petersen, Springer Series in Statistics, Targeted Learning in Data Science, 2018, 195  crossref
    19. Johann Pfanzagl, Springer Series in Statistics, Mathematical Statistics, 2017, 107  crossref
    20. Hui Chen, Winston Wei Dou, Leonid Kogan, “Measuring the 'Dark Matter' in Asset Pricing Models”, SSRN Journal, 2013  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
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