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Teor. Veroyatnost. i Primenen., 1964, Volume 9, Issue 1, Pages 157–159 (Mi tvp356)  

This article is cited in 11 scientific papers (total in 11 papers)

Short Communications

On Estimating Regression

E. A. Nadaraya

Tbilisi

Abstract: A study is made of certain properties of an approximation to the regression line on the basis of sampling data when the sample size increases unboundedly.

Full text: PDF file (183 kB)

English version:
Theory of Probability and its Applications, 1964, 9:1, 141–142

Bibliographic databases:

Received: 27.09.1963

Citation: E. A. Nadaraya, “On Estimating Regression”, Teor. Veroyatnost. i Primenen., 9:1 (1964), 157–159; Theory Probab. Appl., 9:1 (1964), 141–142

Citation in format AMSBIB
\Bibitem{Nad64}
\by E.~A.~Nadaraya
\paper On Estimating Regression
\jour Teor. Veroyatnost. i Primenen.
\yr 1964
\vol 9
\issue 1
\pages 157--159
\mathnet{http://mi.mathnet.ru/tvp356}
\mathscinet{http://www.ams.org/mathscinet-getitem?mr=166874}
\zmath{https://zbmath.org/?q=an:0136.40902}
\transl
\jour Theory Probab. Appl.
\yr 1964
\vol 9
\issue 1
\pages 141--142
\crossref{https://doi.org/10.1137/1109020}


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    Citing articles on Google Scholar: Russian citations, English citations
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    This publication is cited in the following articles:
    1. A. V. Kitaeva, G. M. Koshkin, “Recurrent nonparametric estimation of functions from functionals of multidimensional density and their derivatives”, Autom. Remote Control, 70:3 (2009), 389–407  mathnet  crossref  mathscinet  zmath  isi
    2. A. V. Kitaeva, G. M. Koshkin, “Nonparametric semirecursive identification in a wide sense of strong mixing processes”, Problems Inform. Transmission, 46:1 (2010), 22–37  mathnet  crossref  mathscinet  isi
    3. A. V. Kitaeva, G. M. Koshkin, “Semi-recursive nonparametric identification in the general sense of a nonlinear heteroscedastic autoregression”, Autom. Remote Control, 71:2 (2010), 257–274  mathnet  crossref  mathscinet  zmath  isi
    4. Skripin S.V., “Statisticheskaya otsenka vesovogo koeffitsienta v kombinirovannoi modeli regressii”, Izvestiya tomskogo politekhnicheskogo universiteta, 319:5 (2011), 19–24  elib
    5. È. A. Nadaraya, P. Babilua, G. A. Sokhadze, “On the integral square deviation of one nonparametric estimation of the Bernoulli regession”, Theory Probab. Appl., 57:2 (2013), 265–278  mathnet  crossref  crossref  mathscinet  zmath  isi  elib
    6. Kitaeva A.V., Subbotina V.I., “Smeschenie yadernykh otsenok funktsionalov ot uslovnykh raspredelenii: znakoperemennye yadra i polinomialnaya approksimatsiya”, Vestnik tomskogo gosudarstvennogo universiteta. upravlenie, vychislitelnaya tekhnika i informatika, 2012, no. 4, 61–70  elib
    7. Dmitriev Yu.G., Koshkin G.M., Lukov V.Yu., “Combined Identification and Prediction Algorithms”, Proceedings of the Iv International Research Conference Information Technologies in Science, Management, Social Sphere and Medicine (Itsmssm 2017), Acsr-Advances in Comptuer Science Research, 72, eds. Berestneva O., Tikhomirov A., Trufanov A., Kataev M., Atlantis Press, 2017, 244–247  isi
    8. Grasmair M., Li H., Munk A., “Variational Multiscale Nonparametric Regression: Smooth Functions”, Ann. Inst. Henri Poincare-Probab. Stat., 54:2 (2018), 1058–1097  crossref  isi
    9. Younso A., “On the Consistency of Kernel Classification Rule For Functional Random Field”, J. SFdS, 159:1 (2018), 68–87  isi
    10. Chen G.H. Shah D., “Explaining the Success of Nearest Neighbor Methods in Prediction”, Found. Trends Mach. Learn., 10:5-6 (2018), 337–588  crossref  isi
    11. Krumscheid S., “Perturbation-Based Inference For Diffusion Processes: Obtaining Effective Models From Multiscale Data”, Math. Models Meth. Appl. Sci., 28:8 (2018), 1565–1597  crossref  isi
  • Теория вероятностей и ее применения Theory of Probability and its Applications
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