O. V. Lepskii, “Asymptotically minimax adaptive estimation. I. Upper bounds. Optimally adaptive estimates”, Teor. Veroyatnost. i Primenen., 36:4 (1991), 645–659; Theory Probab. Appl., 36:4 (1991), 682

Teoriya Veroyatnostei i ee Primeneniya

RUS ENG

JOURNALS PEOPLE ORGANISATIONS CONFERENCES SEMINARS VIDEO LIBRARY PACKAGE AMSBIB

JavaScript is disabled in your browser. Please switch it on to enable full functionality of the website

	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, 1991, Volume 36, Issue 4, Pages 645–659 (Mi tvp1773)

This article is cited in 111 scientific papers (total in 111 papers)

Asymptotically minimax adaptive estimation. I. Upper bounds. Optimally adaptive estimates

O. V. Lepskii

Full-text PDF (966 kB) Citations (111)

Received: 09.01.1989

English version:
Theory of Probability and its Applications, 1991, Volume 36, Issue 4, Pages 682–697
DOI: https://doi.org/10.1137/1136085

Bibliographic databases:

Language: Russian

Citation: O. V. Lepskii, “Asymptotically minimax adaptive estimation. I. Upper bounds. Optimally adaptive estimates”, Teor. Veroyatnost. i Primenen., 36:4 (1991), 645–659; Theory Probab. Appl., 36:4 (1991), 682–697

Citation in format AMSBIB

\Bibitem{Lep91}

\by O.~V.~Lepskii

\paper Asymptotically minimax adaptive estimation. I.~Upper bounds. Optimally adaptive estimates

\jour Teor. Veroyatnost. i Primenen.

\yr 1991

\vol 36

\issue 4

\pages 645--659

\mathnet{http://mi.mathnet.ru/tvp1773}

\mathscinet{http://mathscinet.ams.org/mathscinet-getitem?mr=1147167}

\zmath{https://zbmath.org/?q=an:0776.62039|0738.62045}

\transl

\jour Theory Probab. Appl.

\yr 1991

\vol 36

\issue 4

\pages 682--697

\crossref{https://doi.org/10.1137/1136085}

\isi{https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=Publons&SrcAuth=Publons_CEL&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=A1991KG46300003}

Linking options:

https://www.mathnet.ru/eng/tvp1773

https://www.mathnet.ru/eng/tvp/v36/i4/p645

Cycle of papers

Asymptotically minimax adaptive estimation. I. Upper bounds. Optimally adaptive estimates
O. V. Lepskii
Teor. Veroyatnost. i Primenen., 1991, 36:4, 645–659
Asymptotically Minimax Adaptive Estimation. II. Schemes without Optimal Adaptation: Adaptive Estimators
O. V. Lepskiĭ
Teor. Veroyatnost. i Primenen., 1992, 37:3, 468–481

This publication is cited in the following 111 articles:

Yaroslav Averyanov, Alain Celisse, “Minimum discrepancy principle strategy for choosing k in k-NN regression”, Mach Learn, 114:5 (2025)
Thorsten Hohage, Pierre Maréchal, Léopold Simar, Anne Vanhems, “A MOLLIFIER APPROACH TO THE DECONVOLUTION OF PROBABILITY DENSITIES”, Econom. Theory, 40:2 (2024), 320
Weidong Liu, Xiaojun Mao, Xiaofei Zhang, Xin Zhang, “Robust Personalized Federated Learning with Sparse Penalization”, Journal of the American Statistical Association, 2024, 1
Changxiao Cai, T. Tony Cai, Hongzhe Li, “Transfer learning for contextual multi-armed bandits”, Ann. Statist., 52:1 (2024)
Sergios Agapiou, Ismaël Castillo, “Heavy-tailed Bayesian nonparametric adaptation”, Ann. Statist., 52:4 (2024)
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison J. Hu, Ryan J. Tibshirani, “Multivariate trend filtering for lattice data”, Ann. Statist., 52:5 (2024)
Dominik Rothenhäusler, “Model selection and inference for estimation of causal parameters”, Electron. J. Statist., 18:2 (2024)
Anton Rask Lundborg, Ilmun Kim, Rajen D. Shah, Richard J. Samworth, “The projected covariance measure for assumption-lean variable significance testing”, Ann. Statist., 52:6 (2024)
Mathieu Sart, “Density estimation under local differential privacy and Hellinger loss”, Bernoulli, 29:3 (2023)
Yuchen Hu, Stefan Wager, “Off-policy evaluation in partially observed Markov decision processes under sequential ignorability”, Ann. Statist., 51:4 (2023)
Peiliang Zhang, Zhao Ren, “Adaptive minimax density estimation on ℝd for Huber's contamination model”, Information and Inference: A Journal of the IMA, 12:4 (2023), 3042
Yang Ning, Guang Cheng, “Sparse confidence sets for normal mean models”, Information and Inference: A Journal of the IMA, 12:3 (2023), 1193
Matias D. Cattaneo, Yingjie Feng, William G. Underwood, “Uniform Inference for Kernel Density Estimators with Dyadic Data”, Journal of the American Statistical Association, 2023, 1
Luc Devroye, Silvio Lattanzi, Gábor Lugosi, Nikita Zhivotovskiy, “On mean estimation for heteroscedastic random variables”, Ann. Inst. H. Poincaré Probab. Statist., 59:1 (2023)
Cathrine Aeckerle-Willems, Claudia Strauch, “Sup-norm adaptive drift estimation for multivariate nonreversible diffusions”, Ann. Statist., 50:6 (2022)
Alexander Goldenshluger, Oleg V. Lepski, “Minimax estimation of norms of a probability density: II. Rate-optimal estimation procedures”, Bernoulli, 28:2 (2022)
Jules Depersin, Guillaume Lecué, “Robust sub-Gaussian estimation of a mean vector in nearly linear time”, Ann. Statist., 50:1 (2022)
Arnak S. Dalalyan, Arshak Minasyan, “All-in-one robust estimator of the Gaussian mean”, Ann. Statist., 50:2 (2022)
Yonatan Gur, Ahmadreza Momeni, Stefan Wager, “Smoothness-Adaptive Contextual Bandits”, Operations Research, 70:6 (2022), 3198
Page S. T. E. P. H. E. N. Grunewalder S. T. E. F. F. E. N., “The Goldenshluger-Lepski Method For Constrained Least-Squares Estimators Over Rkhss”, Bernoulli, 27:4 (2021), 2241–2266

Citing articles in Google Scholar: Russian citations, English citations
Related articles in Google Scholar: Russian articles, English articles

Theory of Probability and its Applications

Statistics & downloads:
Abstract page:	695
Full-text PDF :	352

Что такое QR-код?

Registration to the website

Logotypes