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Teor. Veroyatnost. i Primenen., 2012, Volume 57, Issue 2, Pages 257–277 (Mi tvp4446)  

This article is cited in 7 scientific papers (total in 7 papers)

General procedure for selecting linear estimators

A. V. Gol'denshlyugera, O. V. Lepskiĭb

a Institute of Automatics, National Academy of Sciences
b Université de Provence Aix-Marseille I

DOI: https://doi.org/10.4213/tvp4446

Full text: PDF file (248 kB)
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English version:
Theory of Probability and its Applications, 2013, 57:2, 209–226

Bibliographic databases:

Received: 19.10.2011

Citation: A. V. Gol'denshlyuger, O. V. Lepskiǐ, “General procedure for selecting linear estimators”, Teor. Veroyatnost. i Primenen., 57:2 (2012), 257–277; Theory Probab. Appl., 57:2 (2013), 209–226

Citation in format AMSBIB
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    Citing articles on Google Scholar: Russian citations, English citations
    Related articles on Google Scholar: Russian articles, English articles

    This publication is cited in the following articles:
    1. O. Lepski, “Adaptive estimation over anisotropic functional classes via oracle approach”, Ann. Stat., 43:3 (2015), 1178–1242  crossref  mathscinet  zmath  isi
    2. C. Lacour, P. Massart, “Minimal penalty for Goldenshluger–Lepski method”, Stoch. Process. Their Appl., 126:12, SI (2016), 3774–3789  crossref  mathscinet  zmath  isi  scopus
    3. K. Bertin, C. Lacour, V. Rivoirard, “Adaptive pointwise estimation of conditional density function”, Ann. Inst. Henri Poincare-Probab. Stat., 52:2 (2016), 939–980  crossref  mathscinet  zmath  isi  scopus
    4. C. Lacour, P. Massart, V. Rivoirard, “Estimator selection: a new method with applications to kernel density estimation”, Sankhya Ser. A, 79:2, SI (2017), 298–335  crossref  mathscinet  zmath  isi
    5. O. V. Lepski, “A new approach to estimator selection”, Bernoulli, 24:4A (2018), 2776–2810  crossref  mathscinet  zmath  isi
    6. G. H. Chen, D. Shah, “Explaining the success of nearest neighbor methods in prediction”, Found. Trends Mach. Learn., 10:5-6 (2018), 337–588  crossref  isi
    7. Lehericy L., “State-By-State Minimax Adaptive Estimation For Nonparametric Hidden Markov Models”, J. Mach. Learn. Res., 19 (2018), 1  isi
  • Теория вероятностей и ее применения Theory of Probability and its Applications
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